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Fix warnings when running AutoMate env #3660

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Fix warnings when running AutoMate env #3660

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72388bc
fix the warning from assembly_env.py
yijieg Oct 8, 2025
4060cb4
fix warnings in soft_dtw_cuda.py
yijieg Oct 8, 2025
706e30b
fix NumbaPerformanceWarning
yijieg Oct 8, 2025
00fa5da
remove redundant code
yijieg Oct 8, 2025
0eb6210
minor fix on disassembly trajectory collection
yijieg Oct 8, 2025
5d1ee06
fix format
yijieg Oct 9, 2025
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43 changes: 7 additions & 36 deletions source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_env.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,19 +66,6 @@ def __init__(self, cfg: AssemblyEnvCfg, render_mode: str | None = None, **kwargs
if self.cfg_task.if_logging_eval:
self._init_eval_logging()

if self.cfg_task.sample_from != "rand":
self._init_eval_loading()

def _init_eval_loading(self):
eval_held_asset_pose, eval_fixed_asset_pose, eval_success = automate_log.load_log_from_hdf5(
self.cfg_task.eval_filename
)

if self.cfg_task.sample_from == "gp":
self.gp = automate_algo.model_succ_w_gp(eval_held_asset_pose, eval_fixed_asset_pose, eval_success)
elif self.cfg_task.sample_from == "gmm":
self.gmm = automate_algo.model_succ_w_gmm(eval_held_asset_pose, eval_fixed_asset_pose, eval_success)

def _init_eval_logging(self):

self.held_asset_pose_log = torch.empty(
Expand Down Expand Up @@ -246,7 +233,7 @@ def _load_disassembly_data(self):
# offset each trajectory to be relative to the goal
eef_pos_traj.append(curr_ee_traj - curr_ee_goal)

self.eef_pos_traj = torch.tensor(eef_pos_traj, dtype=torch.float32, device=self.device).squeeze()
self.eef_pos_traj = torch.tensor(np.array(eef_pos_traj), dtype=torch.float32, device=self.device).squeeze()

def _get_keypoint_offsets(self, num_keypoints):
"""Get uniformly-spaced keypoints along a line of unit length, centered at 0."""
Expand Down Expand Up @@ -804,28 +791,12 @@ def randomize_held_initial_state(self, env_ids, pre_grasp):
torch.rand((self.num_envs,), dtype=torch.float32, device=self.device)
)

if self.cfg_task.sample_from == "rand":

rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device)
held_pos_init_rand = 2 * (rand_sample - 0.5) # [-1, 1]
held_asset_init_pos_rand = torch.tensor(
self.cfg_task.held_asset_init_pos_noise, dtype=torch.float32, device=self.device
)
self.held_pos_init_rand = held_pos_init_rand @ torch.diag(held_asset_init_pos_rand)

if self.cfg_task.sample_from == "gp":
rand_sample = torch.rand((self.cfg_task.num_gp_candidates, 3), dtype=torch.float32, device=self.device)
held_pos_init_rand = 2 * (rand_sample - 0.5) # [-1, 1]
held_asset_init_pos_rand = torch.tensor(
self.cfg_task.held_asset_init_pos_noise, dtype=torch.float32, device=self.device
)
held_asset_init_candidates = held_pos_init_rand @ torch.diag(held_asset_init_pos_rand)
self.held_pos_init_rand, _ = automate_algo.propose_failure_samples_batch_from_gp(
self.gp, held_asset_init_candidates.cpu().detach().numpy(), len(env_ids), self.device
)

if self.cfg_task.sample_from == "gmm":
self.held_pos_init_rand = automate_algo.sample_rel_pos_from_gmm(self.gmm, len(env_ids), self.device)
rand_sample = torch.rand((len(env_ids), 3), dtype=torch.float32, device=self.device)
held_pos_init_rand = 2 * (rand_sample - 0.5) # [-1, 1]
held_asset_init_pos_rand = torch.tensor(
self.cfg_task.held_asset_init_pos_noise, dtype=torch.float32, device=self.device
)
self.held_pos_init_rand = held_pos_init_rand @ torch.diag(held_asset_init_pos_rand)

# Set plug pos to assembled state, but offset plug Z-coordinate by height of socket,
# minus curriculum displacement
Expand Down
4 changes: 0 additions & 4 deletions source/isaaclab_tasks/isaaclab_tasks/direct/automate/assembly_tasks_cfg.py
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Expand Up @@ -139,10 +139,6 @@ class AssemblyTask:
num_eval_trials: int = 100
eval_filename: str = "evaluation_00015.h5"

# Fine-tuning
sample_from: str = "rand" # gp, gmm, idv, rand
num_gp_candidates: int = 1000


@configclass
class Peg8mm(HeldAssetCfg):
Expand Down
2 changes: 1 addition & 1 deletion source/isaaclab_tasks/isaaclab_tasks/direct/automate/disassembly_tasks_cfg.py
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Expand Up @@ -118,7 +118,7 @@ class Extraction(DisassemblyTask):
assembly_id = "00015"
assembly_dir = f"{ASSET_DIR}/{assembly_id}/"
disassembly_dir = "disassembly_dir"
num_log_traj = 1000
num_log_traj = 100

fixed_asset_cfg = Hole8mm()
held_asset_cfg = Peg8mm()
Expand Down
4 changes: 2 additions & 2 deletions source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_w_id.py
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Expand Up @@ -61,9 +61,9 @@ def main():

bash_command = None
if sys.platform.startswith("win"):
bash_command = "isaaclab.bat -p"
bash_command = "NUMBA_CUDA_LOW_OCCUPANCY_WARNINGS=0 isaaclab.bat -p"
elif sys.platform.startswith("linux"):
bash_command = "./isaaclab.sh -p"
bash_command = "NUMBA_CUDA_LOW_OCCUPANCY_WARNINGS=0 ./isaaclab.sh -p"
Comment on lines +64 to +66
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What does the NUMBA_CUDA_LOW_OCCUPANCY_WARNINGS do here specifically? Could you add a comment in the code if it needed for something specific?

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This line is a easy fix for the reported warning "/colossus/isaaclab/venv_sim_pip_lab_src/lib/python3.11/site-packages/numba/cuda/dispatcher.py:536: NumbaPerformanceWarning: Grid size 1 will likely result in GPU under-utilization due to low occupancy.
warn(NumbaPerformanceWarning(msg))
[2025-07-23 22:33:10,045][numba.cuda.cudadrv.driver][INFO] - init
/colossus/isaaclab/venv_sim_pip_lab_src/lib/python3.11/site-packages/numba/cuda/dispatcher.py:536: NumbaPerformanceWarning: Grid size 1 will likely result in GPU under-utilization due to low occupancy.
warn(NumbaPerformanceWarning(msg))".

if args.train:
bash_command += " scripts/reinforcement_learning/rl_games/train.py --task=Isaac-AutoMate-Assembly-Direct-v0"
bash_command += f" --seed={str(args.seed)} --max_iterations={str(args.max_iterations)}"
Expand Down
4 changes: 2 additions & 2 deletions source/isaaclab_tasks/isaaclab_tasks/direct/automate/soft_dtw_cuda.py
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Expand Up @@ -123,8 +123,8 @@ class _SoftDTWCUDA(Function):
def forward(ctx, D, gamma, bandwidth):
dev = D.device
dtype = D.dtype
gamma = torch.cuda.FloatTensor([gamma])
bandwidth = torch.cuda.FloatTensor([bandwidth])
gamma = torch.tensor([gamma], dtype=torch.float, device="cuda")
bandwidth = torch.tensor([bandwidth], dtype=torch.float, device="cuda")
Comment on lines +126 to +127
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Why is this using "cuda" and not the dev itself? Not sure if this will work nicely on multi-gpu setup?

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We did not run AutoMate env with multi-gpu setting. Could you please suggest how to change device='cuda' for multi-gpu setting? Any example of this?


B = D.shape[0]
N = D.shape[1]
Expand Down
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