Add planning to grasp API

CHANGELOG.md

@@ -10,17 +10,40 @@ its affiliates is strictly prohibited.
 -->
 # Changelog
 
-## Latest Commit
+## Version 0.7.5
+
+### Changes in Default Behavior
+- Remove explicit global seed setting for numpy and random. To enforce deterministic behavior,
+use `np.random.seed(2)` and `random.seed(2)` in your program.
+- geom.types.VoxelGrid now uses a different algorithm to calculate number of voxels per dimension
+and also to compute xyz locations in a grid. This new implementation matches implementation in
+nvblox.
 
 ### New Features
 - Add pose cost metric to MPC to allow for partial pose reaching.
 - Update obstacle poses in cpu reference with an optional flag.
+- Add planning to grasp API in ``MotionGen.plan_grasp`` that plans a sequence of motions to grasp
+an object given grasp poses. This API also provides args to disable collisions during the grasping
+phase.
+- Constrained planning can now use either goal frame or base frame at runtime.
 
 ### BugFixes & Misc.
 - Fixed optimize_dt not being correctly set when motion gen is called in reactive mode.
 - Add documentation for geom module.
 - Add descriptive api for computing kinematics.
 - Fix cv2 import order in isaac sim realsense examples.
+- Fix attach sphere api mismatch in ``TrajOptSolver``.
+- Fix bug in ``get_spline_interpolated_trajectory`` where
+numpy array was created instead of torch tensor.
+- Fix gradient bug when sphere origin is exactly at face of a cuboid.
+- Add support for parsing Yaml 1.2 format with an updated regex for scientific notations.
+- Move to yaml `SafeLoader` from `Loader`.
+- Graph search checks if a node exists before attempting to find a path.
+- Fix `steps_max` becoming 0 when optimized dt has NaN values.
+- Clone `MotionGenPlanConfig` instance for every plan api.
+- Improve sphere position to voxel location calculation to match nvblox's implementation.
+- Add self collision checking support for spheres > 1024 and number of checks > 512 * 1024.
+- Fix gradient passthrough in warp batch transform kernels.
 
 ## Version 0.7.4
 

benchmark/curobo_benchmark.py

@@ -210,6 +210,7 @@ def load_curobo(
         trajopt_dt=0.25,
         finetune_dt_scale=finetune_dt_scale,
         high_precision=args.high_precision,
+        use_cuda_graph_trajopt_metrics=cuda_graph,
     )
     mg = MotionGen(motion_gen_config)
     mg.warmup(enable_graph=True, warmup_js_trajopt=False, parallel_finetune=parallel_finetune)
@@ -484,7 +485,7 @@ def benchmark_mb(
                             start_state,
                             q_traj,
                             dt=result.interpolation_dt,
-                            save_path=join_path("benchmark/log/usd/", problem_name) + ".usd",
+                            save_path=join_path("benchmark/log/usd/", problem_name)[1:] + ".usd",
                             interpolation_steps=1,
                             write_robot_usd_path="benchmark/log/usd/assets/",
                             robot_usd_local_reference="assets/",
@@ -499,7 +500,7 @@ def benchmark_mb(
                             result.optimized_plan,
                             result.optimized_dt.item(),
                             title=problem_name,
-                            save_path=join_path("benchmark/log/plot/", problem_name + ".png"),
+                            save_path=join_path("benchmark/log/plot/", problem_name + ".png")[1:],
                         )
 
                     m_list.append(current_metrics)

benchmark/curobo_voxel_benchmark.py

@@ -149,7 +149,7 @@ def load_curobo(
                 "base": {
                     "dims": [2.4, 2.4, 2.4],
                     "pose": [0, 0, 0, 1, 0, 0, 0],
-                    "voxel_size": 0.005,
+                    "voxel_size": 0.02,
                     "feature_dtype": torch.bfloat16,
                 },
             }
@@ -294,15 +294,15 @@ def benchmark_mb(
                 world = WorldConfig.from_dict(problem["obstacles"])
 
                 # mg.world_coll_checker.clear_cache()
-                world_coll = WorldConfig.from_dict(problem["obstacles"])
+                world_coll = WorldConfig.from_dict(problem["obstacles"]).get_collision_check_world()
                 if args.mesh:
                     world_coll = world_coll.get_mesh_world(merge_meshes=False)
                 robot_world.clear_world_cache()
                 robot_world.update_world(world_coll)
 
                 esdf = robot_world.world_model.get_esdf_in_bounding_box(
                     Cuboid(name="base", pose=[0, 0, 0, 1, 0, 0, 0], dims=[2.4, 2.4, 2.4]),
-                    voxel_size=0.005,
+                    voxel_size=0.02,
                     dtype=torch.float32,
                 )
                 # esdf.feature_tensor[esdf.feature_tensor < -1.0] = -1000.0
@@ -336,13 +336,16 @@ def benchmark_mb(
                     world.randomize_color(r=[0.5, 0.9], g=[0.2, 0.5], b=[0.0, 0.2])
 
                     coll_mesh = mg.world_coll_checker.get_mesh_in_bounding_box(
-                        curobo_Cuboid(name="test", pose=[0, 0, 0, 1, 0, 0, 0], dims=[2, 2, 2]),
-                        voxel_size=0.005,
+                        curobo_Cuboid(
+                            name="test", pose=[0, 0, 0, 1, 0, 0, 0], dims=[2.4, 2.4, 2.4]
+                        ),
+                        voxel_size=0.02,
                     )
 
                     coll_mesh.color = [0.0, 0.8, 0.8, 0.2]
 
                     coll_mesh.name = "voxel_world"
+
                     # world = WorldConfig(mesh=[coll_mesh])
                     world.add_obstacle(coll_mesh)
                 # get costs:
@@ -360,7 +363,7 @@ def benchmark_mb(
                         plot_cost_iteration(
                             traj_cost,
                             title=problem_name + "_" + str(success) + "_" + str(dt),
-                            save_path=join_path("benchmark/log/plot/", problem_name + "_cost"),
+                            save_path=join_path("benchmark/log/plot/", problem_name + "_cost")[1:],
                             log_scale=False,
                         )
                         if "finetune_trajopt_result" in result.debug_info:
@@ -373,7 +376,7 @@ def benchmark_mb(
                                 title=problem_name + "_" + str(success) + "_" + str(dt),
                                 save_path=join_path(
                                     "benchmark/log/plot/", problem_name + "_ft_cost"
-                                ),
+                                )[1:],
                                 log_scale=False,
                             )
                 if result.success.item():
@@ -481,7 +484,7 @@ def benchmark_mb(
                             start_state,
                             q_traj,
                             dt=result.interpolation_dt,
-                            save_path=join_path("benchmark/log/usd/", problem_name) + ".usd",
+                            save_path=join_path("benchmark/log/usd/", problem_name)[1:] + ".usd",
                             interpolation_steps=1,
                             write_robot_usd_path="benchmark/log/usd/assets/",
                             robot_usd_local_reference="assets/",
@@ -499,15 +502,17 @@ def benchmark_mb(
                             # result.get_interpolated_plan(),
                             # result.interpolation_dt,
                             title=problem_name,
-                            save_path=join_path("benchmark/log/plot/", problem_name + ".pdf"),
+                            save_path=join_path("benchmark/log/plot/", problem_name + ".pdf")[1:],
                         )
                         plot_traj(
                             # result.optimized_plan,
                             # result.optimized_dt.item(),
                             result.get_interpolated_plan(),
                             result.interpolation_dt,
                             title=problem_name,
-                            save_path=join_path("benchmark/log/plot/", problem_name + "_int.pdf"),
+                            save_path=join_path("benchmark/log/plot/", problem_name + "_int.pdf")[
+                                1:
+                            ],
                         )
 
                     m_list.append(current_metrics)

examples/usd_example.py

@@ -19,7 +19,7 @@
 from curobo.types.base import TensorDeviceType
 from curobo.types.math import Pose
 from curobo.types.robot import JointState, RobotConfig
-from curobo.util.logger import setup_curobo_logger
+from curobo.util.logger import setup_curobo_logger, log_error
 from curobo.util.usd_helper import UsdHelper
 from curobo.util_file import (
     get_assets_path,
@@ -44,7 +44,7 @@ def save_curobo_world_to_usd():
     usd_helper.write_stage_to_file("test.usd")
 
 
-def get_trajectory(robot_file="franka.yml", dt=1.0 / 60.0):
+def get_trajectory(robot_file="franka.yml", dt=1.0 / 60.0, plan_grasp: bool = False):
     tensor_args = TensorDeviceType()
     world_file = "collision_test.yml"
     motion_gen_config = MotionGenConfig.load_from_robot_config(
@@ -60,32 +60,48 @@ def get_trajectory(robot_file="franka.yml", dt=1.0 / 60.0):
         interpolation_dt=dt,
     )
     motion_gen = MotionGen(motion_gen_config)
-    motion_gen.warmup()
+    motion_gen.warmup(n_goalset=2)
     robot_cfg = load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
     robot_cfg = RobotConfig.from_dict(robot_cfg, tensor_args)
     retract_cfg = motion_gen.get_retract_config()
     state = motion_gen.rollout_fn.compute_kinematics(
         JointState.from_position(retract_cfg.view(1, -1))
     )
+    if plan_grasp:
+        retract_pose = Pose(
+            state.ee_pos_seq.view(1, -1, 3), quaternion=state.ee_quat_seq.view(1, -1, 4)
+        )
+        start_state = JointState.from_position(retract_cfg.view(1, -1).clone())
+        start_state.position[..., :-2] += 0.5
+        m_config = MotionGenPlanConfig(False, True)
+
+        result = motion_gen.plan_grasp(start_state, retract_pose, m_config.clone())
+        if not result.success:
+            log_error("Failed to plan grasp: " + result.status)
+        traj = result.grasp_interpolated_trajectory
+        traj2 = result.retract_interpolated_trajectory
+        traj = traj.stack(traj2).clone()
+        # result = motion_gen.plan_single(start_state, retract_pose)
+        # traj = result.get_interpolated_plan()  # optimized plan
 
-    retract_pose = Pose(state.ee_pos_seq.squeeze(), quaternion=state.ee_quat_seq.squeeze())
-    start_state = JointState.from_position(retract_cfg.view(1, -1).clone())
-    start_state.position[..., :-2] += 0.5
-    result = motion_gen.plan_single(start_state, retract_pose)
-    traj = result.get_interpolated_plan()  # optimized plan
+    else:
+        retract_pose = Pose(state.ee_pos_seq.squeeze(), quaternion=state.ee_quat_seq.squeeze())
+        start_state = JointState.from_position(retract_cfg.view(1, -1).clone())
+        start_state.position[..., :-2] += 0.5
+        result = motion_gen.plan_single(start_state, retract_pose)
+        traj = result.get_interpolated_plan()  # optimized plan
     return traj
 
 
-def save_curobo_robot_world_to_usd(robot_file="franka.yml"):
-    print(robot_file)
+def save_curobo_robot_world_to_usd(robot_file="franka.yml", plan_grasp: bool = False):
     tensor_args = TensorDeviceType()
     world_file = "collision_test.yml"
     world_model = WorldConfig.from_dict(
         load_yaml(join_path(get_world_configs_path(), world_file))
     ).get_obb_world()
     dt = 1 / 60.0
 
-    q_traj = get_trajectory(robot_file, dt)
+    q_traj = get_trajectory(robot_file, dt, plan_grasp)
     if q_traj is not None:
         q_start = q_traj[0]
         UsdHelper.write_trajectory_animation_with_robot_usd(

src/curobo/cuda_robot_model/cuda_robot_generator.py

@@ -852,7 +852,11 @@ def _build_collision_model(
 
         if not valid_data:
             use_experimental_kernel = False
-            log_warn("Self Collision checks are greater than 32 * 512, using slower kernel")
+            log_warn(
+                "Self Collision checks are greater than 32 * 512, using slower kernel."
+                + " Number of spheres: "
+                + str(self_collision_distance.shape[0])
+            )
         if use_experimental_kernel:
             self_coll_matrix = torch.zeros((2), device=self.tensor_args.device, dtype=torch.uint8)
         else: