Merge pull request #247 from NVlabs/fix_trajectory_evaluation
This commit is contained in:
@@ -10,6 +10,12 @@ its affiliates is strictly prohibited.
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# Changelog
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# Changelog
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## Latest Commit
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### BugFixes & Misc.
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- Fix bug in evaluator to account for dof maximum acceleration and jerk.
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- Add unit test for different acceleration and jerk limits.
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## Version 0.7.2
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## Version 0.7.2
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### New Features
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### New Features
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@@ -81,7 +81,7 @@ class JointState(State):
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@staticmethod
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@staticmethod
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def from_numpy(
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def from_numpy(
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joint_names: List[str],
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joint_names: List[str],
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position: np.ndarry,
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position: np.ndarray,
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velocity: Optional[np.ndarray] = None,
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velocity: Optional[np.ndarray] = None,
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acceleration: Optional[np.ndarray] = None,
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acceleration: Optional[np.ndarray] = None,
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jerk: Optional[np.ndarray] = None,
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jerk: Optional[np.ndarray] = None,
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@@ -91,6 +91,8 @@ class JointState(State):
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vel = acc = je = None
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vel = acc = je = None
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if velocity is not None:
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if velocity is not None:
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vel = tensor_args.to_device(velocity)
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vel = tensor_args.to_device(velocity)
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else:
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vel = pos * 0.0
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if acceleration is not None:
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if acceleration is not None:
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acc = tensor_args.to_device(acceleration)
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acc = tensor_args.to_device(acceleration)
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else:
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else:
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@@ -218,12 +218,13 @@ def compute_smoothness(
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scale_dt = (1 / dt_score).view(-1, 1, 1)
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scale_dt = (1 / dt_score).view(-1, 1, 1)
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abs_acc = torch.abs(acc) * (scale_dt**2)
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abs_acc = torch.abs(acc) * (scale_dt**2)
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# mean_acc_val = torch.max(torch.mean(abs_acc, dim=-1), dim=-1)[0]
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# mean_acc_val = torch.max(torch.mean(abs_acc, dim=-1), dim=-1)[0]
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max_acc_val = torch.max(torch.max(abs_acc, dim=-1)[0], dim=-1)[0]
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max_acc_val = torch.max(abs_acc, dim=-2)[0] # batch x dof
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abs_jerk = torch.abs(jerk) * scale_dt**3
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abs_jerk = torch.abs(jerk) * scale_dt**3
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# calculate max mean jerk:
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# calculate max mean jerk:
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# mean_jerk_val = torch.max(torch.mean(abs_jerk, dim=-1), dim=-1)[0]
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# mean_jerk_val = torch.max(torch.mean(abs_jerk, dim=-1), dim=-1)[0]
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max_jerk_val = torch.max(torch.max(abs_jerk, dim=-1)[0], dim=-1)[0]
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max_jerk_val = torch.max(abs_jerk, dim=-2)[0] # batch x dof
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acc_label = torch.logical_and(max_acc_val <= max_acc, max_jerk_val <= max_jerk)
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acc_label = torch.logical_and(max_acc_val <= max_acc, max_jerk_val <= max_jerk)
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acc_label = torch.all(acc_label, dim=-1)
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return (acc_label, smooth_cost(abs_acc, abs_jerk, dt_score))
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return (acc_label, smooth_cost(abs_acc, abs_jerk, dt_score))
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72
tests/motion_gen_eval_test.py
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72
tests/motion_gen_eval_test.py
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@@ -0,0 +1,72 @@
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#
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# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
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# property and proprietary rights in and to this material, related
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# documentation and any modifications thereto. Any use, reproduction,
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# disclosure or distribution of this material and related documentation
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# without an express license agreement from NVIDIA CORPORATION or
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# its affiliates is strictly prohibited.
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#
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# Third Party
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import pytest
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import torch
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# CuRobo
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from curobo.types.base import TensorDeviceType
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from curobo.types.robot import JointState
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from curobo.util_file import get_robot_configs_path, join_path, load_yaml
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from curobo.wrap.reacher.evaluator import TrajEvaluatorConfig
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from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
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def run_motion_gen(robot_file, evaluate_interpolated_trajectory, max_acc, max_jerk):
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tensor_args = TensorDeviceType()
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world_file = "collision_test.yml"
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robot_data = load_yaml(join_path(get_robot_configs_path(), robot_file))
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dof = len(robot_data["robot_cfg"]["kinematics"]["cspace"]["joint_names"])
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robot_data["robot_cfg"]["kinematics"]["cspace"]["max_acceleration"] = [
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max_acc for i in range(9)
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]
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robot_data["robot_cfg"]["kinematics"]["cspace"]["max_jerk"] = [max_jerk for i in range(9)]
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motion_gen_config = MotionGenConfig.load_from_robot_config(
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robot_data,
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world_file,
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tensor_args,
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use_cuda_graph=False,
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maximum_trajectory_dt=1.5,
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evaluate_interpolated_trajectory=evaluate_interpolated_trajectory,
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)
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motion_gen = MotionGen(motion_gen_config)
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retract_cfg = motion_gen.get_retract_config()
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start_state = JointState.from_position(retract_cfg.view(1, -1).clone())
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goal_state = JointState.from_position(retract_cfg.view(1, -1).clone() + 0.2)
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result = motion_gen.plan_single_js(
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start_state, goal_state, MotionGenPlanConfig(max_attempts=5, enable_graph_attempt=10)
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)
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return result
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@pytest.mark.parametrize(
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"robot_file, evaluate_interpolated_traj, max_acc, max_jerk",
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[
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("franka.yml", False, 1.0, 500.0),
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("franka.yml", True, 0.1, 500.0),
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("franka.yml", True, 1.0, 500.0),
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("ur5e.yml", False, 1.0, 500.0),
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("ur5e.yml", True, 0.1, 500.0),
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("ur5e.yml", True, 1.0, 500.0),
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],
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)
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def test_motion_gen_trajectory(robot_file, evaluate_interpolated_traj, max_acc, max_jerk):
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result = run_motion_gen(robot_file, evaluate_interpolated_traj, max_acc, max_jerk)
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assert result.success.item()
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assert torch.max(torch.abs(result.optimized_plan.acceleration)) <= max_acc
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assert torch.max(torch.abs(result.optimized_plan.jerk)) <= max_jerk
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