Add support for older warp versions (<1.0.0)

CHANGELOG.md

@@ -10,6 +10,14 @@ its affiliates is strictly prohibited.
 -->
 # Changelog
 
+## Latest Commit
+
+### BugFixes & Misc.
+- Add support for older warp versions (<1.0.0) as it's not possible to run older isaac sim with
+newer warp versions.
+- Add override option to mpc dataclass.
+- Fix bug in ``PoseCost.forward_pose()`` which caused ``torch_layers_example.py`` to fail.
+
 ## Version 0.7.3
 
 ### New Features

README.md

@@ -19,7 +19,11 @@ Use [Discussions](https://github.com/NVlabs/curobo/discussions) for questions on
 Use [Issues](https://github.com/NVlabs/curobo/issues) if you find a bug.
 
 
-For business inquiries, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/)
+cuRobo's collision-free motion planner is available for commercial applications as a
+MoveIt plugin: [Isaac ROS cuMotion](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_cumotion)
+
+For business inquiries of this python library, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/)
+
 
 ## Overview
 

examples/torch_layers_example.py

@@ -85,10 +85,13 @@ class CuroboTorch(torch.nn.Module):
             kin_state.ee_quaternion,
         ]
         if x_des is not None:
-            pose_distance = self._robot_world.pose_distance(x_des, kin_state.ee_pose)
+            pose_distance = self._robot_world.pose_distance(
+                x_des, kin_state.ee_pose, resize=True
+            ).view(-1, 1)
             features.append(pose_distance)
             features.append(x_des.position)
             features.append(x_des.quaternion)
+
         features = torch.cat(features, dim=-1)
 
         return features
@@ -114,17 +117,25 @@ class CuroboTorch(torch.nn.Module):
 
     def loss(self, x_des: Pose, q: torch.Tensor, q_in: torch.Tensor):
         kin_state = self._robot_world.get_kinematics(q)
-        distance = self._robot_world.pose_distance(x_des, kin_state.ee_pose)
+        distance = self._robot_world.pose_distance(x_des, kin_state.ee_pose, resize=True)
-        d_sdf = self._robot_world.collision_constraint(kin_state.link_spheres_tensor.unsqueeze(1))
+        d_sdf = self._robot_world.collision_constraint(
-        d_self = self._robot_world.self_collision_cost(kin_state.link_spheres_tensor.unsqueeze(1))
+            kin_state.link_spheres_tensor.unsqueeze(1)
+        ).view(-1)
+        d_self = self._robot_world.self_collision_cost(
+            kin_state.link_spheres_tensor.unsqueeze(1)
+        ).view(-1)
         loss = 0.1 * torch.linalg.norm(q_in - q, dim=-1) + distance + 100.0 * (d_self + d_sdf)
         return loss
 
     def val_loss(self, x_des: Pose, q: torch.Tensor, q_in: torch.Tensor):
         kin_state = self._robot_world.get_kinematics(q)
-        distance = self._robot_world.pose_distance(x_des, kin_state.ee_pose)
+        distance = self._robot_world.pose_distance(x_des, kin_state.ee_pose, resize=True)
-        d_sdf = self._robot_world.collision_constraint(kin_state.link_spheres_tensor.unsqueeze(1))
+        d_sdf = self._robot_world.collision_constraint(
-        d_self = self._robot_world.self_collision_cost(kin_state.link_spheres_tensor.unsqueeze(1))
+            kin_state.link_spheres_tensor.unsqueeze(1)
+        ).view(-1)
+        d_self = self._robot_world.self_collision_cost(
+            kin_state.link_spheres_tensor.unsqueeze(1)
+        ).view(-1)
         loss = 10.0 * (d_self + d_sdf) + distance
         return loss
 

setup.cfg

@@ -50,7 +50,7 @@ install_requires =
   torch>=1.10
   trimesh
   yourdfpy>=0.0.53
-  warp-lang>=0.11.0
+  warp-lang>=0.9.0
   scipy>=1.7.0
   tqdm
   wheel

src/curobo/geom/sdf/warp_primitives.py

@@ -15,499 +15,16 @@ import warp as wp
 
 wp.set_module_options({"fast_math": False})
 
+# CuRobo
+from curobo.util.warp import warp_support_sdf_struct
 
-@wp.func
+# Check version of warp and import the supported SDF function.
-def mesh_query_point_fn(
+if warp_support_sdf_struct():
-    idx: wp.uint64,
+    # Local Folder
-    point: wp.vec3,
+    from .warp_sdf_fns import get_closest_pt_batch_env, get_swept_closest_pt_batch_env
-    max_distance: float,
+else:
-):
+    # Local Folder
-    collide_result = wp.mesh_query_point(idx, point, max_distance)
+    from .warp_sdf_fns_deprecated import get_closest_pt_batch_env, get_swept_closest_pt_batch_env
-    return collide_result
-
-
-@wp.kernel
-def get_swept_closest_pt_batch_env(
-    pt: wp.array(dtype=wp.vec4),
-    distance: wp.array(dtype=wp.float32),  # this stores the output cost
-    closest_pt: wp.array(dtype=wp.float32),  # this stores the gradient
-    sparsity_idx: wp.array(dtype=wp.uint8),
-    weight: wp.array(dtype=wp.float32),
-    activation_distance: wp.array(dtype=wp.float32),  # eta threshold
-    speed_dt: wp.array(dtype=wp.float32),
-    mesh: wp.array(dtype=wp.uint64),
-    mesh_pose: wp.array(dtype=wp.float32),
-    mesh_enable: wp.array(dtype=wp.uint8),
-    n_env_mesh: wp.array(dtype=wp.int32),
-    max_dist: wp.array(dtype=wp.float32),
-    write_grad: wp.uint8,
-    batch_size: wp.int32,
-    horizon: wp.int32,
-    nspheres: wp.int32,
-    max_nmesh: wp.int32,
-    sweep_steps: wp.uint8,
-    enable_speed_metric: wp.uint8,
-    env_query_idx: wp.array(dtype=wp.int32),
-    use_batch_env: wp.uint8,
-):
-    # we launch nspheres kernels
-    # compute gradient here and return
-    # distance is negative outside and positive inside
-    tid = int(0)
-    tid = wp.tid()
-
-    b_idx = int(0)
-    h_idx = int(0)
-    sph_idx = int(0)
-    # read horizon
-
-    b_idx = tid / (horizon * nspheres)
-
-    h_idx = (tid - (b_idx * (horizon * nspheres))) / nspheres
-    sph_idx = tid - (b_idx * horizon * nspheres) - (h_idx * nspheres)
-    if b_idx >= batch_size or h_idx >= horizon or sph_idx >= nspheres:
-        return
-    uint_zero = wp.uint8(0)
-    uint_one = wp.uint8(1)
-    env_idx = int(0)
-    n_mesh = int(0)
-    # $wp.printf("%d, %d, %d, %d \n", tid, b_idx, h_idx, sph_idx)
-    # read sphere
-    sphere_0_distance = float(0.0)
-    sphere_2_distance = float(0.0)
-
-    sphere_0 = wp.vec3(0.0)
-    sphere_2 = wp.vec3(0.0)
-    sphere_int = wp.vec3(0.0)
-    sphere_temp = wp.vec3(0.0)
-    grad_vec = wp.vec3(0.0)
-    eta = float(0.0)
-    dt = float(0.0)
-    k0 = float(0.0)
-    sign = float(0.0)
-    dist = float(0.0)
-    dist_metric = float(0.0)
-    euclidean_distance = float(0.0)
-    cl_pt = wp.vec3(0.0)
-    local_pt = wp.vec3(0.0)
-    in_sphere = pt[b_idx * horizon * nspheres + (h_idx * nspheres) + sph_idx]
-    in_rad = in_sphere[3]
-    if in_rad < 0.0:
-        distance[tid] = 0.0
-        if write_grad == 1 and sparsity_idx[tid] == uint_one:
-            sparsity_idx[tid] = uint_zero
-            closest_pt[tid * 4] = 0.0
-            closest_pt[tid * 4 + 1] = 0.0
-            closest_pt[tid * 4 + 2] = 0.0
-
-        return
-    dt = speed_dt[0]
-    eta = activation_distance[0]
-    in_rad += eta
-    max_dist_buffer = float(0.0)
-    max_dist_buffer = max_dist[0]
-    if (in_rad) > max_dist_buffer:
-        max_dist_buffer += in_rad
-
-    in_pt = wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
-    # read in sphere 0:
-    if h_idx > 0:
-        in_sphere = pt[b_idx * horizon * nspheres + ((h_idx - 1) * nspheres) + sph_idx]
-        sphere_0 += wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
-        sphere_0_distance = wp.length(sphere_0 - in_pt) / 2.0
-    if h_idx < horizon - 1:
-        in_sphere = pt[b_idx * horizon * nspheres + ((h_idx + 1) * nspheres) + sph_idx]
-        sphere_2 += wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
-        sphere_2_distance = wp.length(sphere_2 - in_pt) / 2.0
-
-    # read in sphere 2:
-    closest_distance = float(0.0)
-    closest_point = wp.vec3(0.0)
-    i = int(0)
-    dis_length = float(0.0)
-    jump_distance = float(0.0)
-    mid_distance = float(0.0)
-    if use_batch_env:
-        env_idx = env_query_idx[b_idx]
-    i = max_nmesh * env_idx
-    n_mesh = i + n_env_mesh[env_idx]
-    obj_position = wp.vec3()
-
-    while i < n_mesh:
-        if mesh_enable[i] == uint_one:
-            # transform point to mesh frame:
-            # mesh_pt = T_inverse @ w_pt
-            obj_position[0] = mesh_pose[i * 8 + 0]
-            obj_position[1] = mesh_pose[i * 8 + 1]
-            obj_position[2] = mesh_pose[i * 8 + 2]
-            obj_quat = wp.quaternion(
-                mesh_pose[i * 8 + 4],
-                mesh_pose[i * 8 + 5],
-                mesh_pose[i * 8 + 6],
-                mesh_pose[i * 8 + 3],
-            )
-
-            obj_w_pose = wp.transform(obj_position, obj_quat)
-            obj_w_pose_t = wp.transform_inverse(obj_w_pose)
-            local_pt = wp.transform_point(obj_w_pose, in_pt)
-            collide_result = mesh_query_point_fn(mesh[i], local_pt, max_dist_buffer)
-            if collide_result.result:
-                sign = collide_result.sign
-                cl_pt = wp.mesh_eval_position(
-                    mesh[i], collide_result.face, collide_result.u, collide_result.v
-                )
-                delta = cl_pt - local_pt
-                dis_length = wp.length(delta)
-                dist = (-1.0 * dis_length * sign) + in_rad
-                if dist > 0:
-                    if dist == in_rad:
-                        cl_pt = sign * (delta) / (dist)
-                    else:
-                        cl_pt = sign * (delta) / dis_length
-                    euclidean_distance = dist
-                    if dist > eta:
-                        dist_metric = dist - 0.5 * eta
-                    elif dist <= eta:
-                        dist_metric = (0.5 / eta) * (dist) * dist
-                        cl_pt = (1.0 / eta) * dist * cl_pt
-                    grad_vec = wp.transform_vector(obj_w_pose_t, cl_pt)
-
-                    closest_distance += dist_metric
-                    closest_point += grad_vec
-                else:
-                    dist = -1.0 * dist
-                    euclidean_distance = dist
-            else:
-                dist = max_dist_buffer
-                euclidean_distance = dist
-            dist = max(euclidean_distance - in_rad, in_rad)
-
-            mid_distance = euclidean_distance
-            # transform sphere -1
-            if h_idx > 0 and mid_distance < sphere_0_distance:
-                jump_distance = mid_distance
-                j = int(0)
-                sphere_temp = wp.transform_point(obj_w_pose, sphere_0)
-                while j < sweep_steps:
-                    k0 = (
-                        1.0 - 0.5 * jump_distance / sphere_0_distance
-                    )  # dist could be greater than sphere_0_distance here?
-                    sphere_int = k0 * local_pt + ((1.0 - k0) * sphere_temp)
-                    collide_result = mesh_query_point_fn(mesh[i], sphere_int, max_dist_buffer)
-                    if collide_result.result:
-                        sign = collide_result.sign
-                        cl_pt = wp.mesh_eval_position(
-                            mesh[i], collide_result.face, collide_result.u, collide_result.v
-                        )
-                        delta = cl_pt - sphere_int
-                        dis_length = wp.length(delta)
-                        dist = (-1.0 * dis_length * sign) + in_rad
-                        if dist > 0:
-                            if dist == in_rad:
-                                cl_pt = sign * (delta) / (dist)
-                            else:
-                                cl_pt = sign * (delta) / dis_length
-                            euclidean_distance = dist
-                            if dist > eta:
-                                dist_metric = dist - 0.5 * eta
-                            elif dist <= eta:
-                                dist_metric = (0.5 / eta) * (dist) * dist
-                                cl_pt = (1.0 / eta) * dist * cl_pt
-
-                            closest_distance += dist_metric
-                            grad_vec = wp.transform_vector(obj_w_pose_t, cl_pt)
-
-                            closest_point += grad_vec
-                            dist = max(euclidean_distance - in_rad, in_rad)
-                            jump_distance += euclidean_distance
-                        else:
-                            dist = max(-dist - in_rad, in_rad)
-                            euclidean_distance = dist
-                            jump_distance += euclidean_distance
-                    else:
-                        jump_distance += max_dist_buffer
-                    j += 1
-                    if jump_distance >= sphere_0_distance:
-                        j = int(sweep_steps)
-            # transform sphere -1
-            if h_idx < horizon - 1 and mid_distance < sphere_2_distance:
-                jump_distance = mid_distance
-                j = int(0)
-                sphere_temp = wp.transform_point(obj_w_pose, sphere_2)
-                while j < sweep_steps:
-                    k0 = (
-                        1.0 - 0.5 * jump_distance / sphere_2_distance
-                    )  # dist could be greater than sphere_0_distance here?
-                    sphere_int = k0 * local_pt + (1.0 - k0) * sphere_temp
-                    collide_result = mesh_query_point_fn(mesh[i], sphere_int, max_dist_buffer)
-                    if collide_result.result:
-                        sign = collide_result.sign
-                        cl_pt = wp.mesh_eval_position(
-                            mesh[i], collide_result.face, collide_result.u, collide_result.v
-                        )
-                        delta = cl_pt - sphere_int
-                        dis_length = wp.length(delta)
-                        dist = (-1.0 * dis_length * sign) + in_rad
-                        if dist > 0:
-                            euclidean_distance = dist
-                            if dist == in_rad:
-                                cl_pt = sign * (delta) / (dist)
-                            else:
-                                cl_pt = sign * (delta) / dis_length
-                            # cl_pt = sign * (delta) / dis_length
-                            if dist > eta:
-                                dist_metric = dist - 0.5 * eta
-                            elif dist <= eta:
-                                dist_metric = (0.5 / eta) * (dist) * dist
-                                cl_pt = (1.0 / eta) * dist * cl_pt
-                            closest_distance += dist_metric
-                            grad_vec = wp.transform_vector(obj_w_pose_t, cl_pt)
-
-                            closest_point += grad_vec
-                            dist = max(euclidean_distance - in_rad, in_rad)
-                            jump_distance += dist
-
-                        else:
-                            dist = max(-dist - in_rad, in_rad)
-                            jump_distance += dist
-                    else:
-                        jump_distance += max_dist_buffer
-
-                    j += 1
-                    if jump_distance >= sphere_2_distance:
-                        j = int(sweep_steps)
-        i += 1
-
-    # return
-    if closest_distance <= 0.0:
-        if sparsity_idx[tid] == uint_zero:
-            return
-        sparsity_idx[tid] = uint_zero
-        distance[tid] = 0.0
-        if write_grad == 1:
-            closest_pt[tid * 4 + 0] = 0.0
-            closest_pt[tid * 4 + 1] = 0.0
-            closest_pt[tid * 4 + 2] = 0.0
-
-        return
-    if enable_speed_metric == 1 and (h_idx > 0 and h_idx < horizon - 1):
-        # calculate sphere velocity and acceleration:
-        norm_vel_vec = wp.vec3(0.0)
-        sph_acc_vec = wp.vec3(0.0)
-        sph_vel = wp.float(0.0)
-
-        # use central difference
-        norm_vel_vec = (0.5 / dt) * (sphere_2 - sphere_0)
-        sph_vel = wp.length(norm_vel_vec)
-        if sph_vel > 1e-3:
-            sph_acc_vec = (1.0 / (dt * dt)) * (sphere_0 + sphere_2 - 2.0 * in_pt)
-
-            norm_vel_vec = norm_vel_vec * (1.0 / sph_vel)
-
-            curvature_vec = sph_acc_vec / (sph_vel * sph_vel)
-
-            orth_proj = wp.mat33(0.0)
-            for i in range(3):
-                for j in range(3):
-                    orth_proj[i, j] = -1.0 * norm_vel_vec[i] * norm_vel_vec[j]
-
-            orth_proj[0, 0] = orth_proj[0, 0] + 1.0
-            orth_proj[1, 1] = orth_proj[1, 1] + 1.0
-            orth_proj[2, 2] = orth_proj[2, 2] + 1.0
-
-            orth_curv = wp.vec3(
-                0.0, 0.0, 0.0
-            )  # closest_distance * (orth_proj @ curvature_vec) #wp.matmul(orth_proj, curvature_vec)
-            orth_pt = wp.vec3(0.0, 0.0, 0.0)  # orth_proj @ closest_point
-            for i in range(3):
-                orth_pt[i] = (
-                    orth_proj[i, 0] * closest_point[0]
-                    + orth_proj[i, 1] * closest_point[1]
-                    + orth_proj[i, 2] * closest_point[2]
-                )
-
-                orth_curv[i] = closest_distance * (
-                    orth_proj[i, 0] * curvature_vec[0]
-                    + orth_proj[i, 1] * curvature_vec[1]
-                    + orth_proj[i, 2] * curvature_vec[2]
-                )
-
-            closest_point = sph_vel * (orth_pt - orth_curv)
-
-            closest_distance = sph_vel * closest_distance
-
-    distance[tid] = weight[0] * closest_distance
-    sparsity_idx[tid] = uint_one
-    if write_grad == 1:
-        # compute gradient:
-        closest_distance = weight[0]
-        closest_pt[tid * 4 + 0] = closest_distance * closest_point[0]
-        closest_pt[tid * 4 + 1] = closest_distance * closest_point[1]
-        closest_pt[tid * 4 + 2] = closest_distance * closest_point[2]
-
-
-@wp.kernel
-def get_closest_pt_batch_env(
-    pt: wp.array(dtype=wp.vec4),
-    distance: wp.array(dtype=wp.float32),  # this stores the output cost
-    closest_pt: wp.array(dtype=wp.float32),  # this stores the gradient
-    sparsity_idx: wp.array(dtype=wp.uint8),
-    weight: wp.array(dtype=wp.float32),
-    activation_distance: wp.array(dtype=wp.float32),  # eta threshold
-    mesh: wp.array(dtype=wp.uint64),
-    mesh_pose: wp.array(dtype=wp.float32),
-    mesh_enable: wp.array(dtype=wp.uint8),
-    n_env_mesh: wp.array(dtype=wp.int32),
-    max_dist: wp.array(dtype=wp.float32),
-    write_grad: wp.uint8,
-    batch_size: wp.int32,
-    horizon: wp.int32,
-    nspheres: wp.int32,
-    max_nmesh: wp.int32,
-    env_query_idx: wp.array(dtype=wp.int32),
-    use_batch_env: wp.uint8,
-    compute_esdf: wp.uint8,
-):
-    # we launch nspheres kernels
-    # compute gradient here and return
-    # distance is negative outside and positive inside
-    tid = wp.tid()
-    n_mesh = int(0)
-    b_idx = int(0)
-    h_idx = int(0)
-    sph_idx = int(0)
-
-    env_idx = int(0)
-
-    b_idx = tid / (horizon * nspheres)
-
-    h_idx = (tid - (b_idx * (horizon * nspheres))) / nspheres
-    sph_idx = tid - (b_idx * horizon * nspheres) - (h_idx * nspheres)
-    if b_idx >= batch_size or h_idx >= horizon or sph_idx >= nspheres:
-        return
-
-    sign = float(0.0)
-    dist = float(0.0)
-    grad_vec = wp.vec3(0.0)
-    eta = float(0.0)
-    dist_metric = float(0.0)
-    max_dist_buffer = float(0.0)
-    uint_zero = wp.uint8(0)
-    uint_one = wp.uint8(1)
-    cl_pt = wp.vec3(0.0)
-    local_pt = wp.vec3(0.0)
-    in_sphere = pt[tid]
-    in_pt = wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
-    in_rad = in_sphere[3]
-    if in_rad < 0.0:
-        distance[tid] = 0.0
-        if write_grad == 1 and sparsity_idx[tid] == uint_one:
-            sparsity_idx[tid] = uint_zero
-            closest_pt[tid * 4] = 0.0
-            closest_pt[tid * 4 + 1] = 0.0
-            closest_pt[tid * 4 + 2] = 0.0
-
-        return
-    eta = activation_distance[0]
-    if compute_esdf != 1:
-        in_rad += eta
-    max_dist_buffer = max_dist[0]
-    if (in_rad) > max_dist_buffer:
-        max_dist_buffer += in_rad
-
-    # TODO: read vec4 and use first 3 for sphere position and last one for radius
-    # in_pt = pt[tid]
-    closest_distance = float(0.0)
-    closest_point = wp.vec3(0.0)
-    dis_length = float(0.0)
-
-    # read env index:
-    if use_batch_env:
-        env_idx = env_query_idx[b_idx]
-
-    # read number of boxes in current environment:
-
-    # get start index
-    i = int(0)
-    i = max_nmesh * env_idx
-    n_mesh = i + n_env_mesh[env_idx]
-    obj_position = wp.vec3()
-    max_dist_value = -1.0 * max_dist_buffer
-    if compute_esdf == 1:
-        closest_distance = max_dist_value
-    while i < n_mesh:
-        if mesh_enable[i] == uint_one:
-            # transform point to mesh frame:
-            # mesh_pt = T_inverse @ w_pt
-            obj_position[0] = mesh_pose[i * 8 + 0]
-            obj_position[1] = mesh_pose[i * 8 + 1]
-            obj_position[2] = mesh_pose[i * 8 + 2]
-            obj_quat = wp.quaternion(
-                mesh_pose[i * 8 + 4],
-                mesh_pose[i * 8 + 5],
-                mesh_pose[i * 8 + 6],
-                mesh_pose[i * 8 + 3],
-            )
-
-            obj_w_pose = wp.transform(obj_position, obj_quat)
-
-            local_pt = wp.transform_point(obj_w_pose, in_pt)
-            collide_result = mesh_query_point_fn(mesh[i], local_pt, max_dist_buffer)
-            if collide_result.result:
-                sign = collide_result.sign
-                cl_pt = wp.mesh_eval_position(
-                    mesh[i], collide_result.face, collide_result.u, collide_result.v
-                )
-                delta = cl_pt - local_pt
-                dis_length = wp.length(delta)
-                dist = -1.0 * dis_length * sign
-
-                if compute_esdf == 1:
-                    if dist > max_dist_value:
-                        max_dist_value = dist
-                        closest_distance = dist
-                        if write_grad == 1:
-                            cl_pt = sign * (delta) / dis_length
-                            grad_vec = wp.transform_vector(wp.transform_inverse(obj_w_pose), cl_pt)
-                            closest_point = grad_vec
-
-                else:
-                    dist = dist + in_rad
-
-                    if dist > 0:
-                        if dist == in_rad:
-                            cl_pt = sign * (delta) / (dist)
-                        else:
-                            cl_pt = sign * (delta) / dis_length
-                        if dist > eta:
-                            dist_metric = dist - 0.5 * eta
-                        elif dist <= eta:
-                            dist_metric = (0.5 / eta) * (dist) * dist
-                            cl_pt = (1.0 / eta) * dist * cl_pt
-                        grad_vec = wp.transform_vector(wp.transform_inverse(obj_w_pose), cl_pt)
-
-                        closest_distance += dist_metric
-                        closest_point += grad_vec
-        i += 1
-
-    if closest_distance == 0 and compute_esdf != 1:
-        if sparsity_idx[tid] == uint_zero:
-            return
-        sparsity_idx[tid] = uint_zero
-        distance[tid] = 0.0
-        if write_grad == 1:
-            closest_pt[tid * 4 + 0] = 0.0
-            closest_pt[tid * 4 + 1] = 0.0
-            closest_pt[tid * 4 + 2] = 0.0
-    else:
-        distance[tid] = weight[0] * closest_distance
-        sparsity_idx[tid] = uint_one
-        if write_grad == 1:
-            # compute gradient:
-            closest_distance = weight[0]
-            closest_pt[tid * 4 + 0] = closest_distance * closest_point[0]
-            closest_pt[tid * 4 + 1] = closest_distance * closest_point[1]
-            closest_pt[tid * 4 + 2] = closest_distance * closest_point[2]
 
 
 class SdfMeshWarpPy(torch.autograd.Function):

src/curobo/geom/sdf/warp_sdf_fns.py

@@ -0,0 +1,506 @@
+#
+# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+#
+# Third Party
+import warp as wp
+
+
+@wp.func
+def mesh_query_point_fn(
+    idx: wp.uint64,
+    point: wp.vec3,
+    max_distance: float,
+):
+    collide_result = wp.mesh_query_point(idx, point, max_distance)
+    return collide_result
+
+
+@wp.kernel
+def get_swept_closest_pt_batch_env(
+    pt: wp.array(dtype=wp.vec4),
+    distance: wp.array(dtype=wp.float32),  # this stores the output cost
+    closest_pt: wp.array(dtype=wp.float32),  # this stores the gradient
+    sparsity_idx: wp.array(dtype=wp.uint8),
+    weight: wp.array(dtype=wp.float32),
+    activation_distance: wp.array(dtype=wp.float32),  # eta threshold
+    speed_dt: wp.array(dtype=wp.float32),
+    mesh: wp.array(dtype=wp.uint64),
+    mesh_pose: wp.array(dtype=wp.float32),
+    mesh_enable: wp.array(dtype=wp.uint8),
+    n_env_mesh: wp.array(dtype=wp.int32),
+    max_dist: wp.array(dtype=wp.float32),
+    write_grad: wp.uint8,
+    batch_size: wp.int32,
+    horizon: wp.int32,
+    nspheres: wp.int32,
+    max_nmesh: wp.int32,
+    sweep_steps: wp.uint8,
+    enable_speed_metric: wp.uint8,
+    env_query_idx: wp.array(dtype=wp.int32),
+    use_batch_env: wp.uint8,
+):
+    # we launch nspheres kernels
+    # compute gradient here and return
+    # distance is negative outside and positive inside
+    tid = int(0)
+    tid = wp.tid()
+
+    b_idx = int(0)
+    h_idx = int(0)
+    sph_idx = int(0)
+    # read horizon
+
+    b_idx = tid / (horizon * nspheres)
+
+    h_idx = (tid - (b_idx * (horizon * nspheres))) / nspheres
+    sph_idx = tid - (b_idx * horizon * nspheres) - (h_idx * nspheres)
+    if b_idx >= batch_size or h_idx >= horizon or sph_idx >= nspheres:
+        return
+    uint_zero = wp.uint8(0)
+    uint_one = wp.uint8(1)
+    env_idx = int(0)
+    n_mesh = int(0)
+    # $wp.printf("%d, %d, %d, %d \n", tid, b_idx, h_idx, sph_idx)
+    # read sphere
+    sphere_0_distance = float(0.0)
+    sphere_2_distance = float(0.0)
+
+    sphere_0 = wp.vec3(0.0)
+    sphere_2 = wp.vec3(0.0)
+    sphere_int = wp.vec3(0.0)
+    sphere_temp = wp.vec3(0.0)
+    grad_vec = wp.vec3(0.0)
+    eta = float(0.0)
+    dt = float(0.0)
+    k0 = float(0.0)
+    sign = float(0.0)
+    dist = float(0.0)
+    dist_metric = float(0.0)
+    euclidean_distance = float(0.0)
+    cl_pt = wp.vec3(0.0)
+    local_pt = wp.vec3(0.0)
+    in_sphere = pt[b_idx * horizon * nspheres + (h_idx * nspheres) + sph_idx]
+    in_rad = in_sphere[3]
+    if in_rad < 0.0:
+        distance[tid] = 0.0
+        if write_grad == 1 and sparsity_idx[tid] == uint_one:
+            sparsity_idx[tid] = uint_zero
+            closest_pt[tid * 4] = 0.0
+            closest_pt[tid * 4 + 1] = 0.0
+            closest_pt[tid * 4 + 2] = 0.0
+
+        return
+    dt = speed_dt[0]
+    eta = activation_distance[0]
+    in_rad += eta
+    max_dist_buffer = float(0.0)
+    max_dist_buffer = max_dist[0]
+    if (in_rad) > max_dist_buffer:
+        max_dist_buffer += in_rad
+
+    in_pt = wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
+    # read in sphere 0:
+    if h_idx > 0:
+        in_sphere = pt[b_idx * horizon * nspheres + ((h_idx - 1) * nspheres) + sph_idx]
+        sphere_0 += wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
+        sphere_0_distance = wp.length(sphere_0 - in_pt) / 2.0
+    if h_idx < horizon - 1:
+        in_sphere = pt[b_idx * horizon * nspheres + ((h_idx + 1) * nspheres) + sph_idx]
+        sphere_2 += wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
+        sphere_2_distance = wp.length(sphere_2 - in_pt) / 2.0
+
+    # read in sphere 2:
+    closest_distance = float(0.0)
+    closest_point = wp.vec3(0.0)
+    i = int(0)
+    dis_length = float(0.0)
+    jump_distance = float(0.0)
+    mid_distance = float(0.0)
+    if use_batch_env:
+        env_idx = env_query_idx[b_idx]
+    i = max_nmesh * env_idx
+    n_mesh = i + n_env_mesh[env_idx]
+    obj_position = wp.vec3()
+
+    while i < n_mesh:
+        if mesh_enable[i] == uint_one:
+            # transform point to mesh frame:
+            # mesh_pt = T_inverse @ w_pt
+            obj_position[0] = mesh_pose[i * 8 + 0]
+            obj_position[1] = mesh_pose[i * 8 + 1]
+            obj_position[2] = mesh_pose[i * 8 + 2]
+            obj_quat = wp.quaternion(
+                mesh_pose[i * 8 + 4],
+                mesh_pose[i * 8 + 5],
+                mesh_pose[i * 8 + 6],
+                mesh_pose[i * 8 + 3],
+            )
+
+            obj_w_pose = wp.transform(obj_position, obj_quat)
+            obj_w_pose_t = wp.transform_inverse(obj_w_pose)
+            local_pt = wp.transform_point(obj_w_pose, in_pt)
+            collide_result = mesh_query_point_fn(mesh[i], local_pt, max_dist_buffer)
+            if collide_result.result:
+                sign = collide_result.sign
+                cl_pt = wp.mesh_eval_position(
+                    mesh[i], collide_result.face, collide_result.u, collide_result.v
+                )
+                delta = cl_pt - local_pt
+                dis_length = wp.length(delta)
+                dist = (-1.0 * dis_length * sign) + in_rad
+                if dist > 0:
+                    if dist == in_rad:
+                        cl_pt = sign * (delta) / (dist)
+                    else:
+                        cl_pt = sign * (delta) / dis_length
+                    euclidean_distance = dist
+                    if dist > eta:
+                        dist_metric = dist - 0.5 * eta
+                    elif dist <= eta:
+                        dist_metric = (0.5 / eta) * (dist) * dist
+                        cl_pt = (1.0 / eta) * dist * cl_pt
+                    grad_vec = wp.transform_vector(obj_w_pose_t, cl_pt)
+
+                    closest_distance += dist_metric
+                    closest_point += grad_vec
+                else:
+                    dist = -1.0 * dist
+                    euclidean_distance = dist
+            else:
+                dist = max_dist_buffer
+                euclidean_distance = dist
+            dist = max(euclidean_distance - in_rad, in_rad)
+
+            mid_distance = euclidean_distance
+            # transform sphere -1
+            if h_idx > 0 and mid_distance < sphere_0_distance:
+                jump_distance = mid_distance
+                j = int(0)
+                sphere_temp = wp.transform_point(obj_w_pose, sphere_0)
+                while j < sweep_steps:
+                    k0 = (
+                        1.0 - 0.5 * jump_distance / sphere_0_distance
+                    )  # dist could be greater than sphere_0_distance here?
+                    sphere_int = k0 * local_pt + ((1.0 - k0) * sphere_temp)
+                    collide_result = mesh_query_point_fn(mesh[i], sphere_int, max_dist_buffer)
+                    if collide_result.result:
+                        sign = collide_result.sign
+                        cl_pt = wp.mesh_eval_position(
+                            mesh[i], collide_result.face, collide_result.u, collide_result.v
+                        )
+                        delta = cl_pt - sphere_int
+                        dis_length = wp.length(delta)
+                        dist = (-1.0 * dis_length * sign) + in_rad
+                        if dist > 0:
+                            if dist == in_rad:
+                                cl_pt = sign * (delta) / (dist)
+                            else:
+                                cl_pt = sign * (delta) / dis_length
+                            euclidean_distance = dist
+                            if dist > eta:
+                                dist_metric = dist - 0.5 * eta
+                            elif dist <= eta:
+                                dist_metric = (0.5 / eta) * (dist) * dist
+                                cl_pt = (1.0 / eta) * dist * cl_pt
+
+                            closest_distance += dist_metric
+                            grad_vec = wp.transform_vector(obj_w_pose_t, cl_pt)
+
+                            closest_point += grad_vec
+                            dist = max(euclidean_distance - in_rad, in_rad)
+                            jump_distance += euclidean_distance
+                        else:
+                            dist = max(-dist - in_rad, in_rad)
+                            euclidean_distance = dist
+                            jump_distance += euclidean_distance
+                    else:
+                        jump_distance += max_dist_buffer
+                    j += 1
+                    if jump_distance >= sphere_0_distance:
+                        j = int(sweep_steps)
+            # transform sphere -1
+            if h_idx < horizon - 1 and mid_distance < sphere_2_distance:
+                jump_distance = mid_distance
+                j = int(0)
+                sphere_temp = wp.transform_point(obj_w_pose, sphere_2)
+                while j < sweep_steps:
+                    k0 = (
+                        1.0 - 0.5 * jump_distance / sphere_2_distance
+                    )  # dist could be greater than sphere_0_distance here?
+                    sphere_int = k0 * local_pt + (1.0 - k0) * sphere_temp
+                    collide_result = mesh_query_point_fn(mesh[i], sphere_int, max_dist_buffer)
+                    if collide_result.result:
+                        sign = collide_result.sign
+                        cl_pt = wp.mesh_eval_position(
+                            mesh[i], collide_result.face, collide_result.u, collide_result.v
+                        )
+                        delta = cl_pt - sphere_int
+                        dis_length = wp.length(delta)
+                        dist = (-1.0 * dis_length * sign) + in_rad
+                        if dist > 0:
+                            euclidean_distance = dist
+                            if dist == in_rad:
+                                cl_pt = sign * (delta) / (dist)
+                            else:
+                                cl_pt = sign * (delta) / dis_length
+                            # cl_pt = sign * (delta) / dis_length
+                            if dist > eta:
+                                dist_metric = dist - 0.5 * eta
+                            elif dist <= eta:
+                                dist_metric = (0.5 / eta) * (dist) * dist
+                                cl_pt = (1.0 / eta) * dist * cl_pt
+                            closest_distance += dist_metric
+                            grad_vec = wp.transform_vector(obj_w_pose_t, cl_pt)
+
+                            closest_point += grad_vec
+                            dist = max(euclidean_distance - in_rad, in_rad)
+                            jump_distance += dist
+
+                        else:
+                            dist = max(-dist - in_rad, in_rad)
+                            jump_distance += dist
+                    else:
+                        jump_distance += max_dist_buffer
+
+                    j += 1
+                    if jump_distance >= sphere_2_distance:
+                        j = int(sweep_steps)
+        i += 1
+
+    # return
+    if closest_distance <= 0.0:
+        if sparsity_idx[tid] == uint_zero:
+            return
+        sparsity_idx[tid] = uint_zero
+        distance[tid] = 0.0
+        if write_grad == 1:
+            closest_pt[tid * 4 + 0] = 0.0
+            closest_pt[tid * 4 + 1] = 0.0
+            closest_pt[tid * 4 + 2] = 0.0
+
+        return
+    if enable_speed_metric == 1 and (h_idx > 0 and h_idx < horizon - 1):
+        # calculate sphere velocity and acceleration:
+        norm_vel_vec = wp.vec3(0.0)
+        sph_acc_vec = wp.vec3(0.0)
+        sph_vel = wp.float(0.0)
+
+        # use central difference
+        norm_vel_vec = (0.5 / dt) * (sphere_2 - sphere_0)
+        sph_vel = wp.length(norm_vel_vec)
+        if sph_vel > 1e-3:
+            sph_acc_vec = (1.0 / (dt * dt)) * (sphere_0 + sphere_2 - 2.0 * in_pt)
+
+            norm_vel_vec = norm_vel_vec * (1.0 / sph_vel)
+
+            curvature_vec = sph_acc_vec / (sph_vel * sph_vel)
+
+            orth_proj = wp.mat33(0.0)
+            for i in range(3):
+                for j in range(3):
+                    orth_proj[i, j] = -1.0 * norm_vel_vec[i] * norm_vel_vec[j]
+
+            orth_proj[0, 0] = orth_proj[0, 0] + 1.0
+            orth_proj[1, 1] = orth_proj[1, 1] + 1.0
+            orth_proj[2, 2] = orth_proj[2, 2] + 1.0
+
+            orth_curv = wp.vec3(
+                0.0, 0.0, 0.0
+            )  # closest_distance * (orth_proj @ curvature_vec) #wp.matmul(orth_proj, curvature_vec)
+            orth_pt = wp.vec3(0.0, 0.0, 0.0)  # orth_proj @ closest_point
+            for i in range(3):
+                orth_pt[i] = (
+                    orth_proj[i, 0] * closest_point[0]
+                    + orth_proj[i, 1] * closest_point[1]
+                    + orth_proj[i, 2] * closest_point[2]
+                )
+
+                orth_curv[i] = closest_distance * (
+                    orth_proj[i, 0] * curvature_vec[0]
+                    + orth_proj[i, 1] * curvature_vec[1]
+                    + orth_proj[i, 2] * curvature_vec[2]
+                )
+
+            closest_point = sph_vel * (orth_pt - orth_curv)
+
+            closest_distance = sph_vel * closest_distance
+
+    distance[tid] = weight[0] * closest_distance
+    sparsity_idx[tid] = uint_one
+    if write_grad == 1:
+        # compute gradient:
+        closest_distance = weight[0]
+        closest_pt[tid * 4 + 0] = closest_distance * closest_point[0]
+        closest_pt[tid * 4 + 1] = closest_distance * closest_point[1]
+        closest_pt[tid * 4 + 2] = closest_distance * closest_point[2]
+
+
+@wp.kernel
+def get_closest_pt_batch_env(
+    pt: wp.array(dtype=wp.vec4),
+    distance: wp.array(dtype=wp.float32),  # this stores the output cost
+    closest_pt: wp.array(dtype=wp.float32),  # this stores the gradient
+    sparsity_idx: wp.array(dtype=wp.uint8),
+    weight: wp.array(dtype=wp.float32),
+    activation_distance: wp.array(dtype=wp.float32),  # eta threshold
+    mesh: wp.array(dtype=wp.uint64),
+    mesh_pose: wp.array(dtype=wp.float32),
+    mesh_enable: wp.array(dtype=wp.uint8),
+    n_env_mesh: wp.array(dtype=wp.int32),
+    max_dist: wp.array(dtype=wp.float32),
+    write_grad: wp.uint8,
+    batch_size: wp.int32,
+    horizon: wp.int32,
+    nspheres: wp.int32,
+    max_nmesh: wp.int32,
+    env_query_idx: wp.array(dtype=wp.int32),
+    use_batch_env: wp.uint8,
+    compute_esdf: wp.uint8,
+):
+    # we launch nspheres kernels
+    # compute gradient here and return
+    # distance is negative outside and positive inside
+    tid = wp.tid()
+    n_mesh = int(0)
+    b_idx = int(0)
+    h_idx = int(0)
+    sph_idx = int(0)
+
+    env_idx = int(0)
+
+    b_idx = tid / (horizon * nspheres)
+
+    h_idx = (tid - (b_idx * (horizon * nspheres))) / nspheres
+    sph_idx = tid - (b_idx * horizon * nspheres) - (h_idx * nspheres)
+    if b_idx >= batch_size or h_idx >= horizon or sph_idx >= nspheres:
+        return
+
+    sign = float(0.0)
+    dist = float(0.0)
+    grad_vec = wp.vec3(0.0)
+    eta = float(0.0)
+    dist_metric = float(0.0)
+    max_dist_buffer = float(0.0)
+    uint_zero = wp.uint8(0)
+    uint_one = wp.uint8(1)
+    cl_pt = wp.vec3(0.0)
+    local_pt = wp.vec3(0.0)
+    in_sphere = pt[tid]
+    in_pt = wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
+    in_rad = in_sphere[3]
+    if in_rad < 0.0:
+        distance[tid] = 0.0
+        if write_grad == 1 and sparsity_idx[tid] == uint_one:
+            sparsity_idx[tid] = uint_zero
+            closest_pt[tid * 4] = 0.0
+            closest_pt[tid * 4 + 1] = 0.0
+            closest_pt[tid * 4 + 2] = 0.0
+
+        return
+    eta = activation_distance[0]
+    if compute_esdf != 1:
+        in_rad += eta
+    max_dist_buffer = max_dist[0]
+    if (in_rad) > max_dist_buffer:
+        max_dist_buffer += in_rad
+
+    # TODO: read vec4 and use first 3 for sphere position and last one for radius
+    # in_pt = pt[tid]
+    closest_distance = float(0.0)
+    closest_point = wp.vec3(0.0)
+    dis_length = float(0.0)
+
+    # read env index:
+    if use_batch_env:
+        env_idx = env_query_idx[b_idx]
+
+    # read number of boxes in current environment:
+
+    # get start index
+    i = int(0)
+    i = max_nmesh * env_idx
+    n_mesh = i + n_env_mesh[env_idx]
+    obj_position = wp.vec3()
+    max_dist_value = -1.0 * max_dist_buffer
+    if compute_esdf == 1:
+        closest_distance = max_dist_value
+    while i < n_mesh:
+        if mesh_enable[i] == uint_one:
+            # transform point to mesh frame:
+            # mesh_pt = T_inverse @ w_pt
+            obj_position[0] = mesh_pose[i * 8 + 0]
+            obj_position[1] = mesh_pose[i * 8 + 1]
+            obj_position[2] = mesh_pose[i * 8 + 2]
+            obj_quat = wp.quaternion(
+                mesh_pose[i * 8 + 4],
+                mesh_pose[i * 8 + 5],
+                mesh_pose[i * 8 + 6],
+                mesh_pose[i * 8 + 3],
+            )
+
+            obj_w_pose = wp.transform(obj_position, obj_quat)
+
+            local_pt = wp.transform_point(obj_w_pose, in_pt)
+            collide_result = mesh_query_point_fn(mesh[i], local_pt, max_dist_buffer)
+            if collide_result.result:
+                sign = collide_result.sign
+                cl_pt = wp.mesh_eval_position(
+                    mesh[i], collide_result.face, collide_result.u, collide_result.v
+                )
+                delta = cl_pt - local_pt
+                dis_length = wp.length(delta)
+                dist = -1.0 * dis_length * sign
+
+                if compute_esdf == 1:
+                    if dist > max_dist_value:
+                        max_dist_value = dist
+                        closest_distance = dist
+                        if write_grad == 1:
+                            cl_pt = sign * (delta) / dis_length
+                            grad_vec = wp.transform_vector(wp.transform_inverse(obj_w_pose), cl_pt)
+                            closest_point = grad_vec
+
+                else:
+                    dist = dist + in_rad
+
+                    if dist > 0:
+                        if dist == in_rad:
+                            cl_pt = sign * (delta) / (dist)
+                        else:
+                            cl_pt = sign * (delta) / dis_length
+                        if dist > eta:
+                            dist_metric = dist - 0.5 * eta
+                        elif dist <= eta:
+                            dist_metric = (0.5 / eta) * (dist) * dist
+                            cl_pt = (1.0 / eta) * dist * cl_pt
+                        grad_vec = wp.transform_vector(wp.transform_inverse(obj_w_pose), cl_pt)
+
+                        closest_distance += dist_metric
+                        closest_point += grad_vec
+        i += 1
+
+    if closest_distance == 0 and compute_esdf != 1:
+        if sparsity_idx[tid] == uint_zero:
+            return
+        sparsity_idx[tid] = uint_zero
+        distance[tid] = 0.0
+        if write_grad == 1:
+            closest_pt[tid * 4 + 0] = 0.0
+            closest_pt[tid * 4 + 1] = 0.0
+            closest_pt[tid * 4 + 2] = 0.0
+    else:
+        distance[tid] = weight[0] * closest_distance
+        sparsity_idx[tid] = uint_one
+        if write_grad == 1:
+            # compute gradient:
+            closest_distance = weight[0]
+            closest_pt[tid * 4 + 0] = closest_distance * closest_point[0]
+            closest_pt[tid * 4 + 1] = closest_distance * closest_point[1]
+            closest_pt[tid * 4 + 2] = closest_distance * closest_point[2]

src/curobo/geom/sdf/warp_sdf_fns_deprecated.py

@@ -0,0 +1,496 @@
+#
+# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+#
+# Third Party
+import warp as wp
+
+
+@wp.kernel
+def get_swept_closest_pt_batch_env(
+    pt: wp.array(dtype=wp.vec4),
+    distance: wp.array(dtype=wp.float32),  # this stores the output cost
+    closest_pt: wp.array(dtype=wp.float32),  # this stores the gradient
+    sparsity_idx: wp.array(dtype=wp.uint8),
+    weight: wp.array(dtype=wp.float32),
+    activation_distance: wp.array(dtype=wp.float32),  # eta threshold
+    speed_dt: wp.array(dtype=wp.float32),
+    mesh: wp.array(dtype=wp.uint64),
+    mesh_pose: wp.array(dtype=wp.float32),
+    mesh_enable: wp.array(dtype=wp.uint8),
+    n_env_mesh: wp.array(dtype=wp.int32),
+    max_dist: wp.array(dtype=wp.float32),
+    write_grad: wp.uint8,
+    batch_size: wp.int32,
+    horizon: wp.int32,
+    nspheres: wp.int32,
+    max_nmesh: wp.int32,
+    sweep_steps: wp.uint8,
+    enable_speed_metric: wp.uint8,
+    env_query_idx: wp.array(dtype=wp.int32),
+    use_batch_env: wp.uint8,
+):
+    # we launch nspheres kernels
+    # compute gradient here and return
+    # distance is negative outside and positive inside
+    tid = int(0)
+    tid = wp.tid()
+
+    b_idx = int(0)
+    h_idx = int(0)
+    sph_idx = int(0)
+    # read horizon
+
+    b_idx = tid / (horizon * nspheres)
+
+    h_idx = (tid - (b_idx * (horizon * nspheres))) / nspheres
+    sph_idx = tid - (b_idx * horizon * nspheres) - (h_idx * nspheres)
+    if b_idx >= batch_size or h_idx >= horizon or sph_idx >= nspheres:
+        return
+    uint_zero = wp.uint8(0)
+    uint_one = wp.uint8(1)
+    env_idx = int(0)
+    n_mesh = int(0)
+    # read sphere
+    sphere_0_distance = float(0.0)
+    sphere_2_distance = float(0.0)
+
+    sphere_0 = wp.vec3(0.0)
+    sphere_2 = wp.vec3(0.0)
+    sphere_int = wp.vec3(0.0)
+    sphere_temp = wp.vec3(0.0)
+    grad_vec = wp.vec3(0.0)
+    eta = float(0.0)
+    dt = float(0.0)
+    k0 = float(0.0)
+    face_index = int(0)
+    face_u = float(0.0)
+    face_v = float(0.0)
+    sign = float(0.0)
+    dist = float(0.0)
+    dist_metric = float(0.0)
+    euclidean_distance = float(0.0)
+    cl_pt = wp.vec3(0.0)
+    local_pt = wp.vec3(0.0)
+    in_sphere = pt[b_idx * horizon * nspheres + (h_idx * nspheres) + sph_idx]
+    in_rad = in_sphere[3]
+    if in_rad < 0.0:
+        distance[tid] = 0.0
+        if write_grad == 1 and sparsity_idx[tid] == uint_one:
+            sparsity_idx[tid] = uint_zero
+            closest_pt[tid * 4] = 0.0
+            closest_pt[tid * 4 + 1] = 0.0
+            closest_pt[tid * 4 + 2] = 0.0
+
+        return
+    dt = speed_dt[0]
+    eta = activation_distance[0]
+    in_rad += eta
+    max_dist_buffer = float(0.0)
+    max_dist_buffer = max_dist[0]
+    if (in_rad) > max_dist_buffer:
+        max_dist_buffer += in_rad
+
+    in_pt = wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
+    # read in sphere 0:
+    if h_idx > 0:
+        in_sphere = pt[b_idx * horizon * nspheres + ((h_idx - 1) * nspheres) + sph_idx]
+        sphere_0 += wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
+        sphere_0_distance = wp.length(sphere_0 - in_pt) / 2.0
+    if h_idx < horizon - 1:
+        in_sphere = pt[b_idx * horizon * nspheres + ((h_idx + 1) * nspheres) + sph_idx]
+        sphere_2 += wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
+        sphere_2_distance = wp.length(sphere_2 - in_pt) / 2.0
+
+    # read in sphere 2:
+    closest_distance = float(0.0)
+    closest_point = wp.vec3(0.0)
+    i = int(0)
+    dis_length = float(0.0)
+    jump_distance = float(0.0)
+    mid_distance = float(0.0)
+    if use_batch_env:
+        env_idx = env_query_idx[b_idx]
+    i = max_nmesh * env_idx
+    n_mesh = i + n_env_mesh[env_idx]
+    obj_position = wp.vec3()
+
+    while i < n_mesh:
+        if mesh_enable[i] == uint_one:
+            # transform point to mesh frame:
+            # mesh_pt = T_inverse @ w_pt
+            obj_position[0] = mesh_pose[i * 8 + 0]
+            obj_position[1] = mesh_pose[i * 8 + 1]
+            obj_position[2] = mesh_pose[i * 8 + 2]
+            obj_quat = wp.quaternion(
+                mesh_pose[i * 8 + 4],
+                mesh_pose[i * 8 + 5],
+                mesh_pose[i * 8 + 6],
+                mesh_pose[i * 8 + 3],
+            )
+
+            obj_w_pose = wp.transform(obj_position, obj_quat)
+            obj_w_pose_t = wp.transform_inverse(obj_w_pose)
+            local_pt = wp.transform_point(obj_w_pose, in_pt)
+
+            if wp.mesh_query_point(
+                mesh[i], local_pt, max_dist_buffer, sign, face_index, face_u, face_v
+            ):
+                cl_pt = wp.mesh_eval_position(mesh[i], face_index, face_u, face_v)
+                delta = cl_pt - local_pt
+                dis_length = wp.length(delta)
+                dist = (-1.0 * dis_length * sign) + in_rad
+                if dist > 0:
+                    if dist == in_rad:
+                        cl_pt = sign * (delta) / (dist)
+                    else:
+                        cl_pt = sign * (delta) / dis_length
+                    euclidean_distance = dist
+                    if dist > eta:
+                        dist_metric = dist - 0.5 * eta
+                    elif dist <= eta:
+                        dist_metric = (0.5 / eta) * (dist) * dist
+                        cl_pt = (1.0 / eta) * dist * cl_pt
+                    grad_vec = wp.transform_vector(obj_w_pose_t, cl_pt)
+
+                    closest_distance += dist_metric
+                    closest_point += grad_vec
+                else:
+                    dist = -1.0 * dist
+                    euclidean_distance = dist
+            else:
+                dist = max_dist_buffer
+                euclidean_distance = dist
+            dist = max(euclidean_distance - in_rad, in_rad)
+
+            mid_distance = euclidean_distance
+            # transform sphere -1
+            if h_idx > 0 and mid_distance < sphere_0_distance:
+                jump_distance = mid_distance
+                j = int(0)
+                sphere_temp = wp.transform_point(obj_w_pose, sphere_0)
+                while j < sweep_steps:
+                    k0 = (
+                        1.0 - 0.5 * jump_distance / sphere_0_distance
+                    )  # dist could be greater than sphere_0_distance here?
+                    sphere_int = k0 * local_pt + ((1.0 - k0) * sphere_temp)
+                    if wp.mesh_query_point(
+                        mesh[i], sphere_int, max_dist_buffer, sign, face_index, face_u, face_v
+                    ):
+                        cl_pt = wp.mesh_eval_position(mesh[i], face_index, face_u, face_v)
+                        delta = cl_pt - sphere_int
+                        dis_length = wp.length(delta)
+                        dist = (-1.0 * dis_length * sign) + in_rad
+                        if dist > 0:
+                            if dist == in_rad:
+                                cl_pt = sign * (delta) / (dist)
+                            else:
+                                cl_pt = sign * (delta) / dis_length
+                            euclidean_distance = dist
+                            if dist > eta:
+                                dist_metric = dist - 0.5 * eta
+                            elif dist <= eta:
+                                dist_metric = (0.5 / eta) * (dist) * dist
+                                cl_pt = (1.0 / eta) * dist * cl_pt
+
+                            closest_distance += dist_metric
+                            grad_vec = wp.transform_vector(obj_w_pose_t, cl_pt)
+
+                            closest_point += grad_vec
+                            dist = max(euclidean_distance - in_rad, in_rad)
+                            jump_distance += euclidean_distance
+                        else:
+                            dist = max(-dist - in_rad, in_rad)
+                            euclidean_distance = dist
+                            jump_distance += euclidean_distance
+                    else:
+                        jump_distance += max_dist_buffer
+                    j += 1
+                    if jump_distance >= sphere_0_distance:
+                        j = int(sweep_steps)
+            # transform sphere -1
+            if h_idx < horizon - 1 and mid_distance < sphere_2_distance:
+                jump_distance = mid_distance
+                j = int(0)
+                sphere_temp = wp.transform_point(obj_w_pose, sphere_2)
+                while j < sweep_steps:
+                    k0 = (
+                        1.0 - 0.5 * jump_distance / sphere_2_distance
+                    )  # dist could be greater than sphere_0_distance here?
+                    sphere_int = k0 * local_pt + (1.0 - k0) * sphere_temp
+
+                    if wp.mesh_query_point(
+                        mesh[i], sphere_int, max_dist_buffer, sign, face_index, face_u, face_v
+                    ):
+                        cl_pt = wp.mesh_eval_position(mesh[i], face_index, face_u, face_v)
+                        delta = cl_pt - sphere_int
+                        dis_length = wp.length(delta)
+                        dist = (-1.0 * dis_length * sign) + in_rad
+                        if dist > 0:
+                            euclidean_distance = dist
+                            if dist == in_rad:
+                                cl_pt = sign * (delta) / (dist)
+                            else:
+                                cl_pt = sign * (delta) / dis_length
+                            # cl_pt = sign * (delta) / dis_length
+                            if dist > eta:
+                                dist_metric = dist - 0.5 * eta
+                            elif dist <= eta:
+                                dist_metric = (0.5 / eta) * (dist) * dist
+                                cl_pt = (1.0 / eta) * dist * cl_pt
+                            closest_distance += dist_metric
+                            grad_vec = wp.transform_vector(obj_w_pose_t, cl_pt)
+
+                            closest_point += grad_vec
+                            dist = max(euclidean_distance - in_rad, in_rad)
+                            jump_distance += dist
+
+                        else:
+                            dist = max(-dist - in_rad, in_rad)
+                            jump_distance += dist
+                    else:
+                        jump_distance += max_dist_buffer
+
+                    j += 1
+                    if jump_distance >= sphere_2_distance:
+                        j = int(sweep_steps)
+        i += 1
+
+    # return
+    if closest_distance <= 0.0:
+        if sparsity_idx[tid] == uint_zero:
+            return
+        sparsity_idx[tid] = uint_zero
+        distance[tid] = 0.0
+        if write_grad == 1:
+            closest_pt[tid * 4 + 0] = 0.0
+            closest_pt[tid * 4 + 1] = 0.0
+            closest_pt[tid * 4 + 2] = 0.0
+
+        return
+    if enable_speed_metric == 1 and (h_idx > 0 and h_idx < horizon - 1):
+        # calculate sphere velocity and acceleration:
+        norm_vel_vec = wp.vec3(0.0)
+        sph_acc_vec = wp.vec3(0.0)
+        sph_vel = wp.float(0.0)
+
+        # use central difference
+        norm_vel_vec = (0.5 / dt) * (sphere_2 - sphere_0)
+        sph_vel = wp.length(norm_vel_vec)
+        if sph_vel > 1e-3:
+            sph_acc_vec = (1.0 / (dt * dt)) * (sphere_0 + sphere_2 - 2.0 * in_pt)
+
+            norm_vel_vec = norm_vel_vec * (1.0 / sph_vel)
+
+            curvature_vec = sph_acc_vec / (sph_vel * sph_vel)
+
+            orth_proj = wp.mat33(0.0)
+            for i in range(3):
+                for j in range(3):
+                    orth_proj[i, j] = -1.0 * norm_vel_vec[i] * norm_vel_vec[j]
+
+            orth_proj[0, 0] = orth_proj[0, 0] + 1.0
+            orth_proj[1, 1] = orth_proj[1, 1] + 1.0
+            orth_proj[2, 2] = orth_proj[2, 2] + 1.0
+
+            orth_curv = wp.vec3(
+                0.0, 0.0, 0.0
+            )  # closest_distance * (orth_proj @ curvature_vec) #wp.matmul(orth_proj, curvature_vec)
+            orth_pt = wp.vec3(0.0, 0.0, 0.0)  # orth_proj @ closest_point
+            for i in range(3):
+                orth_pt[i] = (
+                    orth_proj[i, 0] * closest_point[0]
+                    + orth_proj[i, 1] * closest_point[1]
+                    + orth_proj[i, 2] * closest_point[2]
+                )
+
+                orth_curv[i] = closest_distance * (
+                    orth_proj[i, 0] * curvature_vec[0]
+                    + orth_proj[i, 1] * curvature_vec[1]
+                    + orth_proj[i, 2] * curvature_vec[2]
+                )
+
+            closest_point = sph_vel * (orth_pt - orth_curv)
+
+            closest_distance = sph_vel * closest_distance
+
+    distance[tid] = weight[0] * closest_distance
+    sparsity_idx[tid] = uint_one
+    if write_grad == 1:
+        # compute gradient:
+        closest_distance = weight[0]
+        closest_pt[tid * 4 + 0] = closest_distance * closest_point[0]
+        closest_pt[tid * 4 + 1] = closest_distance * closest_point[1]
+        closest_pt[tid * 4 + 2] = closest_distance * closest_point[2]
+
+
+@wp.kernel
+def get_closest_pt_batch_env(
+    pt: wp.array(dtype=wp.vec4),
+    distance: wp.array(dtype=wp.float32),  # this stores the output cost
+    closest_pt: wp.array(dtype=wp.float32),  # this stores the gradient
+    sparsity_idx: wp.array(dtype=wp.uint8),
+    weight: wp.array(dtype=wp.float32),
+    activation_distance: wp.array(dtype=wp.float32),  # eta threshold
+    mesh: wp.array(dtype=wp.uint64),
+    mesh_pose: wp.array(dtype=wp.float32),
+    mesh_enable: wp.array(dtype=wp.uint8),
+    n_env_mesh: wp.array(dtype=wp.int32),
+    max_dist: wp.array(dtype=wp.float32),
+    write_grad: wp.uint8,
+    batch_size: wp.int32,
+    horizon: wp.int32,
+    nspheres: wp.int32,
+    max_nmesh: wp.int32,
+    env_query_idx: wp.array(dtype=wp.int32),
+    use_batch_env: wp.uint8,
+    compute_esdf: wp.uint8,
+):
+    # we launch nspheres kernels
+    # compute gradient here and return
+    # distance is negative outside and positive inside
+    tid = wp.tid()
+    n_mesh = int(0)
+    b_idx = int(0)
+    h_idx = int(0)
+    sph_idx = int(0)
+
+    env_idx = int(0)
+
+    b_idx = tid / (horizon * nspheres)
+
+    h_idx = (tid - (b_idx * (horizon * nspheres))) / nspheres
+    sph_idx = tid - (b_idx * horizon * nspheres) - (h_idx * nspheres)
+    if b_idx >= batch_size or h_idx >= horizon or sph_idx >= nspheres:
+        return
+
+    face_index = int(0)
+    face_u = float(0.0)
+    face_v = float(0.0)
+    sign = float(0.0)
+    dist = float(0.0)
+    grad_vec = wp.vec3(0.0)
+    eta = float(0.0)
+    dist_metric = float(0.0)
+    max_dist_buffer = float(0.0)
+    uint_zero = wp.uint8(0)
+    uint_one = wp.uint8(1)
+    cl_pt = wp.vec3(0.0)
+    local_pt = wp.vec3(0.0)
+    in_sphere = pt[tid]
+    in_pt = wp.vec3(in_sphere[0], in_sphere[1], in_sphere[2])
+    in_rad = in_sphere[3]
+    if in_rad < 0.0:
+        distance[tid] = 0.0
+        if write_grad == 1 and sparsity_idx[tid] == uint_one:
+            sparsity_idx[tid] = uint_zero
+            closest_pt[tid * 4] = 0.0
+            closest_pt[tid * 4 + 1] = 0.0
+            closest_pt[tid * 4 + 2] = 0.0
+
+        return
+    eta = activation_distance[0]
+    if compute_esdf != 1:
+        in_rad += eta
+    max_dist_buffer = max_dist[0]
+    if (in_rad) > max_dist_buffer:
+        max_dist_buffer += in_rad
+
+    # TODO: read vec4 and use first 3 for sphere position and last one for radius
+    # in_pt = pt[tid]
+    closest_distance = float(0.0)
+    closest_point = wp.vec3(0.0)
+    dis_length = float(0.0)
+
+    # read env index:
+    if use_batch_env:
+        env_idx = env_query_idx[b_idx]
+
+    # read number of boxes in current environment:
+
+    # get start index
+    i = int(0)
+    i = max_nmesh * env_idx
+    n_mesh = i + n_env_mesh[env_idx]
+    obj_position = wp.vec3()
+    max_dist_value = -1.0 * max_dist_buffer
+    if compute_esdf == 1:
+        closest_distance = max_dist_value
+    while i < n_mesh:
+        if mesh_enable[i] == uint_one:
+            # transform point to mesh frame:
+            # mesh_pt = T_inverse @ w_pt
+            obj_position[0] = mesh_pose[i * 8 + 0]
+            obj_position[1] = mesh_pose[i * 8 + 1]
+            obj_position[2] = mesh_pose[i * 8 + 2]
+            obj_quat = wp.quaternion(
+                mesh_pose[i * 8 + 4],
+                mesh_pose[i * 8 + 5],
+                mesh_pose[i * 8 + 6],
+                mesh_pose[i * 8 + 3],
+            )
+
+            obj_w_pose = wp.transform(obj_position, obj_quat)
+
+            local_pt = wp.transform_point(obj_w_pose, in_pt)
+
+            if wp.mesh_query_point(
+                mesh[i], local_pt, max_dist_buffer, sign, face_index, face_u, face_v
+            ):
+                cl_pt = wp.mesh_eval_position(mesh[i], face_index, face_u, face_v)
+                delta = cl_pt - local_pt
+                dis_length = wp.length(delta)
+                dist = -1.0 * dis_length * sign
+
+                if compute_esdf == 1:
+                    if dist > max_dist_value:
+                        max_dist_value = dist
+                        closest_distance = dist
+                        if write_grad == 1:
+                            cl_pt = sign * (delta) / dis_length
+                            grad_vec = wp.transform_vector(wp.transform_inverse(obj_w_pose), cl_pt)
+                            closest_point = grad_vec
+
+                else:
+                    dist = dist + in_rad
+
+                    if dist > 0:
+                        if dist == in_rad:
+                            cl_pt = sign * (delta) / (dist)
+                        else:
+                            cl_pt = sign * (delta) / dis_length
+                        if dist > eta:
+                            dist_metric = dist - 0.5 * eta
+                        elif dist <= eta:
+                            dist_metric = (0.5 / eta) * (dist) * dist
+                            cl_pt = (1.0 / eta) * dist * cl_pt
+                        grad_vec = wp.transform_vector(wp.transform_inverse(obj_w_pose), cl_pt)
+
+                        closest_distance += dist_metric
+                        closest_point += grad_vec
+        i += 1
+
+    if closest_distance == 0 and compute_esdf != 1:
+        if sparsity_idx[tid] == uint_zero:
+            return
+        sparsity_idx[tid] = uint_zero
+        distance[tid] = 0.0
+        if write_grad == 1:
+            closest_pt[tid * 4 + 0] = 0.0
+            closest_pt[tid * 4 + 1] = 0.0
+            closest_pt[tid * 4 + 2] = 0.0
+    else:
+        distance[tid] = weight[0] * closest_distance
+        sparsity_idx[tid] = uint_one
+        if write_grad == 1:
+            # compute gradient:
+            closest_distance = weight[0]
+            closest_pt[tid * 4 + 0] = closest_distance * closest_point[0]
+            closest_pt[tid * 4 + 1] = closest_distance * closest_point[1]
+            closest_pt[tid * 4 + 2] = closest_distance * closest_point[2]

src/curobo/rollout/cost/bound_cost.py

@@ -1550,7 +1550,10 @@ def make_bound_pos_smooth_kernel(dof_template: int):
                 out_grad_a[b_addrs + i] = g_a[i]
                 out_grad_j[b_addrs + i] = g_j[i]
 
-    return wp.Kernel(forward_bound_smooth_loop_warp)
+    module = wp.get_module(forward_bound_smooth_loop_warp.__module__)
+    key = "forward_bound_smooth_loop_warp_" + str(dof_template)
+
+    return wp.Kernel(forward_bound_smooth_loop_warp, key=key, module=module)
 
 
 @get_cache_fn_decorator()
@@ -1647,4 +1650,6 @@ def make_bound_pos_kernel(dof_template: int):
             for i in range(dof_template):
                 out_grad_p[b_addrs + i] = g_p[i]
 
-    return wp.Kernel(forward_bound_pos_loop_warp)
+    module = wp.get_module(forward_bound_pos_loop_warp.__module__)
+    key = "forward_bound_pos_loop_warp_" + str(dof_template)
+    return wp.Kernel(forward_bound_pos_loop_warp, key=key, module=module)

src/curobo/rollout/cost/dist_cost.py

@@ -224,7 +224,9 @@ def make_l2_kernel(dof_template: int):
             for i in range(dof_template):
                 out_grad_p[b_addrs + i] = g_p[i]
 
-    return wp.Kernel(forward_l2_loop_warp)
+    module = wp.get_module(forward_l2_loop_warp.__module__)
+    key = "forward_l2_loop" + str(dof_template)
+    return wp.Kernel(forward_l2_loop_warp, key=key, module=module)
 
 
 # create a bound cost tensor:

src/curobo/rollout/cost/pose_cost.py

@@ -466,6 +466,8 @@ class PoseCost(CostBase, PoseCostConfig):
         batch_pose_idx: torch.Tensor,
         mode: PoseErrorType = PoseErrorType.BATCH_GOAL,
     ):
+        if len(query_pose.position.shape) == 2:
+            log_error("Query pose should be [batch, horizon, -1]")
         ee_goal_pos = goal_pose.position
         ee_goal_quat = goal_pose.quaternion
         self.cost_type = mode
@@ -476,9 +478,9 @@ class PoseCost(CostBase, PoseCostConfig):
         num_goals = 1
 
         distance = PoseError.apply(
-            query_pose.position.unsqueeze(1),
+            query_pose.position,
             ee_goal_pos,
-            query_pose.quaternion.unsqueeze(1),
+            query_pose.quaternion,
             ee_goal_quat,
             self.vec_weight,
             self.weight,

src/curobo/util/warp.py

@@ -9,11 +9,14 @@
 # its affiliates is strictly prohibited.
 #
 
+
 # Third Party
 import warp as wp
+from packaging import version
 
 # CuRobo
 from curobo.types.base import TensorDeviceType
+from curobo.util.logger import log_info
 
 
 def init_warp(quiet=True, tensor_args: TensorDeviceType = TensorDeviceType()):
@@ -26,3 +29,19 @@ def init_warp(quiet=True, tensor_args: TensorDeviceType = TensorDeviceType()):
 
     # wp.force_load(wp.device_from_torch(tensor_args.device))
     return True
+
+
+def warp_support_sdf_struct(wp_module=None):
+    if wp_module is None:
+        wp_module = wp
+    wp_version = wp_module.config.version
+
+    if version.parse(wp_version) < version.parse("1.0.0"):
+        log_info(
+            "Warp version is "
+            + wp_version
+            + " < 1.0.0, using older sdf kernels."
+            + "No issues expected."
+        )
+        return False
+    return True

src/curobo/util_file.py

@@ -190,3 +190,11 @@ def get_multi_arm_robot_list() -> List[str]:
         "quad_ur10e.yml",
     ]
     return robot_list
+
+
+def merge_dict_a_into_b(a, b):
+    for k, v in a.items():
+        if isinstance(v, dict):
+            merge_dict_a_into_b(v, b[k])
+        else:
+            b[k] = v

src/curobo/wrap/model/robot_world.py

@@ -343,9 +343,11 @@ class RobotWorld(RobotWorldConfig):
         d_mask = mask(d_self, d_world, d_bound)
         return d_mask
 
-    def pose_distance(self, x_des: Pose, x_current: Pose):
+    def pose_distance(self, x_des: Pose, x_current: Pose, resize: bool = False):
+        unsqueeze = False
         if len(x_current.position.shape) == 2:
             x_current = x_current.unsqueeze(1)
+            unsqueeze = True
         # calculate pose loss:
         if (
             self._batch_pose_idx is None
@@ -355,6 +357,8 @@ class RobotWorld(RobotWorldConfig):
                 0, x_current.position.shape[0], 1, device=self.tensor_args.device, dtype=torch.int32
             )
         distance = self.pose_cost.forward_pose(x_des, x_current, self._batch_pose_idx)
+        if unsqueeze and resize:
+            distance = distance.squeeze(1)
         return distance
 
     def get_point_robot_distance(self, points: torch.Tensor, q: torch.Tensor):

src/curobo/wrap/reacher/mpc.py

@@ -61,6 +61,7 @@ from curobo.util_file import (
     get_world_configs_path,
     join_path,
     load_yaml,
+    merge_dict_a_into_b,
 )
 from curobo.wrap.reacher.types import ReacherSolveState, ReacherSolveType
 from curobo.wrap.wrap_base import WrapResult
@@ -107,6 +108,8 @@ class MpcSolverConfig:
         step_dt: Optional[float] = None,
         use_lbfgs: bool = False,
         use_mppi: bool = True,
+        particle_file: str = "particle_mpc.yml",
+        override_particle_file: str = None,
     ):
         """Create an MPC solver configuration from robot and world configuration.
 
@@ -151,6 +154,9 @@ class MpcSolverConfig:
                 time for a single step.
             use_lbfgs: Use L-BFGS solver for MPC. Highly experimental.
             use_mppi: Use MPPI solver for MPC.
+            particle_file: Particle based MPC config file.
+            override_particle_file: Optional config file for overriding the parameters in the
+                particle based MPC config file.
 
         Returns:
             MpcSolverConfig: Configuration for the MPC solver.
@@ -159,8 +165,9 @@ class MpcSolverConfig:
         if use_cuda_graph_full_step:
             log_error("use_cuda_graph_full_step currently is not supported")
 
-        task_file = "particle_mpc.yml"
+        config_data = load_yaml(join_path(get_task_configs_path(), particle_file))
-        config_data = load_yaml(join_path(get_task_configs_path(), task_file))
+        if override_particle_file is not None:
+            merge_dict_a_into_b(load_yaml(override_particle_file), config_data)
         config_data["mppi"]["n_problems"] = 1
         if step_dt is not None:
             config_data["model"]["dt_traj_params"]["base_dt"] = step_dt