strands-isaaclab-reach-franka

Franka Panda end-effector pose reaching in NVIDIA Isaac Lab — trained by a Strands Agent through strands-robots' isaaclab train_policy provider (PR #4227) and recorded with strands' DatasetRecorder. An agent ran a 6-run hyper-parameter sweep from one prompt and picked this policy; its rollouts are this LeRobot v3 dataset.

4 recorded envs (2×2) from the strands camera — mp4. Policy: cagataydev/strands-isaaclab-reach-franka-policy · the same policy on strands' MuJoCo backend: cagataydev/strands-isaaclab-reach-franka-mujoco-sim2sim.

episodes / frames	12 / 1230 at 30 fps; an episode ends at success (lengths 9 – 360)
camera	observation.images.front 320×240 RTX, fixed
observation.state	48-D = 9 joint pos + 32-D policy observation (policy_obs.*) + root pos (3) + root quat xyzw (4)
action	7-D raw policy action (arm_action.panda_joint1..7, joint-position offsets), 30 Hz
task string	"reach the commanded end-effector pose"
outcome	11 / 12 episodes reached the commanded pose (terminated by success), 1 timed out at 12 s
checks	strands verify_dataset ok · LeRobotDataset load ok · video decode ok · NaN/inf = 0 · Hub round-trip ok

Results

Policy: 4096 envs × 300 PPO iterations (29.5 M env steps) in 4 min 15 s on one NVIDIA L40S, median 117 k env-steps/s (max 126 k). Success rate 0.983 at the last iteration (mean reward -0.25 → 0.02 — see Limitations for why reward is a poor signal here).

PPO iteration	mean reward	success rate	mean ep. length (of 360)	env-steps/s
0	-0.25	0.000	21	36,907
30	-2.87	0.017	360	114,322
75	-1.41	0.309	312	114,751
150	-0.40	0.827	147	122,203
225	-0.37	0.939	104	117,760
299	0.02	0.983	47	119,700

The agent's sweep (6 runs, 1711 s wall, all through train_policy(provider="isaaclab")):

lr	seed	job	final mean reward (what the agent judged)	success rate (Isaac Lab metric, last it)	ee position error
3e-4	1	…edcec0d28a0b	-0.35	0.914	8.0 cm
3e-4	2	…b06eca8dd857	-0.29	0.965	6.5 cm
1e-3	1	…05a8c9c0dbae (this policy)	+0.02	0.983	6.0 cm
1e-3	2	…116af3a5a1bb	-0.51	0.877	9.0 cm
3e-3	1	…41a69ccff38b	-0.34	0.922	8.4 cm
3e-3	2	…e67c06646018	-0.47	0.939	7.5 cm

How it was made with strands-robots

Setup

# Isaac Lab in its OWN venv (its pins clash with strands; strands never imports it)
uv venv --python 3.12 ~/il && uv pip install --python ~/il/bin/python --prerelease=allow \
  --index https://pypi.nvidia.com --index-strategy unsafe-best-match "isaaclab[rsl-rl,isaacsim]==3.0.0rc1"
export ISAACLAB_PYTHON=~/il/bin/python
export OMNI_KIT_ACCEPT_EULA=YES          # you accept the NVIDIA Omniverse / Isaac Sim EULA yourself
pip install "git+https://github.com/cagataycali/robots@feat/isaaclab-trainer" strands-agents   # strands-robots with PR #4227

1 · Train (agent-driven)

This policy was trained by a Strands Agent: one natural-language request → Agent(tools=[train_policy, gpu_status, wait_minutes, stop_job]) (Bedrock Claude) ran a 3 learning-rate × 2 seed sweep of 300-iteration PPO runs through the isaaclab provider, one at a time on the shared GPU, polled them and picked a winner. Script: examples/agent_rl_research_lead.py.

from strands import Agent
from strands_robots.tools.train_policy import train_policy

agent = Agent(tools=[train_policy])          # the example adds gpu_status / wait_minutes / stop_job helpers
agent("Sweep learning rate 3e-4, 1e-3, 3e-3 x seeds 1, 2 on Isaac-Reach-Franka, 4096 envs, 300 iterations each, "
      "one run at a time; poll with status and pick the best.")

The exact tool call the agent made for this run (from agent_transcript.json) — equally usable as plain Python:

train_policy(action="train", provider="isaaclab", steps=300, seed=1, learning_rate=1e-3,
             output_dir="runs/c3_agent_sweep/1e-3_s1",
             extra={"task": "Isaac-Reach-Franka", "num_envs": 4096, "timeout_s": 1800})
train_policy(action="status", provider="isaaclab", job_id="isaaclab-20260929-050417-05a8c9c0dbae")

→ python -m isaaclab train --rl_library rsl_rl --task Isaac-Reach-Franka --max_iterations 300 --num_envs 4096 --seed 1 agent.algorithm.learning_rate=0.001 in $ISAACLAB_PYTHON (task-default physics: Newton / MJWarp). Docs: docs/learn/training/isaaclab.md · PR: strands-labs/robots#4227.

2 · Record with strands DatasetRecorder

Recorded with examples/record_trained_policy.py: rebuild the task env (play mode) with an RTX camera → load model_299.pt with rsl_rl and export TorchScript/ONNX → wrap the actor as a strands Policy (RslRlJitPolicy, max |Δa| vs rsl_rl = 3.0e-07) → step with policy.get_actions_sync(...) → write every frame via strands DatasetRecorder (LeRobot v3) → verify with strands verify_dataset.

OMNI_KIT_ACCEPT_EULA=YES PYTHONPATH=/path/to/strands-robots $ISAACLAB_PYTHON examples/record_trained_policy.py \
  --task Isaac-Reach-Franka --checkpoint model_299.pt --episodes 12 --frames 360 \
  --cam fixed --cam_name front --eye 1.6,0.9,0.9 --target 0.4,0,0.3 \
  --task_str "reach the commanded end-effector pose" --robot_type franka_panda --root out/ds --repo_id cagataydev/strands-isaaclab-reach-franka

Use it

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("cagataydev/strands-isaaclab-reach-franka")
print(ds.num_episodes, ds.num_frames, ds.fps)          # 12 1230 30
f = ds[0]; f["observation.state"].shape, f["action"].shape, f["observation.images.front"].shape
# (48,) (7,) (3, 240, 320)

from huggingface_hub import snapshot_download
from strands_robots.verify_dataset import verify_dataset

report = verify_dataset(snapshot_download("cagataydev/strands-isaaclab-reach-franka", repo_type="dataset"), expected=12)
assert report["ok"], report["problems"]

Train a LeRobot policy on it with strands' lerobot_local provider (behaviour cloning of the RL expert; not run for this card):

from strands_robots.tools.train_policy import train_policy
train_policy(action="train", provider="lerobot_local", dataset_repo_id="cagataydev/strands-isaaclab-reach-franka",
             output_dir="runs/act_reach", steps=20000, batch_size=32, extra={"policy_type": "act"})

Provenance

• strands-robots: feat/isaaclab-trainer @ fa66fc68 — strands-labs/robots#4227 (isaaclab train_policy provider; DatasetRecorder; verify_dataset)
• Isaac Lab 3.0.0rc1 · Isaac Sim 6.1.0.0 · Newton / MJWarp (task default physics) · rsl-rl-lib 5.4.1 (PPO) · lerobot 0.6.1
• GPU: 1× NVIDIA L40S (46 GB), shared with other trainings during the sweep
• Seeds: training seed 1, lr 1e-3 (params/agent.yaml); recording seed 7
• Training job: isaaclab-20260929-050417-05a8c9c0dbae (best of 6 agent-launched runs), 2026-09-29; agent: Strands Agent on Amazon Bedrock (Claude)
• Recording: examples/record_trained_policy.py, play-mode env cfg, 91 s wall

Limitations

• Simulation only; no real Franka was used.
• Release candidates: Isaac Lab 3.0.0rc1 / Isaac Sim 6.1.0.0. Trained on Newton/MJWarp; PhysX results may differ (IL-X-011: the physics preset is not remembered by the checkpoint — pass the same one when replaying).
• Short run (300 iterations, ~4 min). Mean reward ends near 0 and falls mid-training because the task's curriculum ramps the action-rate/joint-velocity penalties and successful episodes terminate early; judge it by success rate. The agent itself misread this and concluded "none of the 6 runs clearly learned" (finding IL-X-012) — its full answer is in agent_answer.md.
• Recording: episodes end at success, so lengths vary (9 – 360 frames); 11/12 reached the target, 1 timed out at 12 s.
• create_policy("rl") cannot load rsl_rl checkpoints yet (IL-X-006); use the exported TorchScript + the wrapper below.

License

Card choice: license: other — our generated data / weights under CC-BY-4.0, plus NVIDIA notices. Why:

• Trajectories, rendered camera video, playback clips, trained weights and agent logs are user-generated content made with NVIDIA Isaac Sim / Isaac Lab; the NVIDIA Omniverse License Agreement (governs Isaac Sim 6.1, isaacsim/LICENSE.txt) §2.1 allows distributing "user generated content that you develop using Omniverse, such as video, audio, stills, models, 3D assets and screen captures". We release it under CC-BY-4.0.
• No NVIDIA Content is redistributed: the Franka Panda USD and scene assets come from the Isaac Lab / Isaac Sim asset packs and are not in this repo (params/env.yaml only references their paths). "Franka" is a trademark of Franka Robotics; no endorsement by Franka Robotics or NVIDIA is implied.
• params/*.yaml are Isaac Lab configurations (BSD-3-Clause); examples/*.py are Apache-2.0 like strands-robots. Running Isaac Sim requires your own acceptance of the NVIDIA Isaac Sim / Omniverse EULA. Full text: LICENSE.md.