---
license: other
license_name: cc-by-4.0-generated-with-nvidia-isaac
license_link: LICENSE.md
tags:
- lerobot
- isaaclab
- isaac-sim
- strands-robots
- robotics
- reinforcement-learning
- franka
- reach
- simulation
- agents
task_categories:
- robotics
pretty_name: Franka Reach — best run of a Strands-Agent-driven Isaac Lab sweep (recorded with strands-robots)
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files: data/*/*.parquet
---

# 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](https://github.com/strands-labs/robots/pull/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.

![playback: 4 recorded envs](playback.gif)

*4 recorded envs (2×2) from the strands camera — [mp4](playback.mp4). Policy: [cagataydev/strands-isaaclab-reach-franka-policy](https://huggingface.co/cagataydev/strands-isaaclab-reach-franka-policy) ·
the same policy on strands' MuJoCo backend: [cagataydev/strands-isaaclab-reach-franka-mujoco-sim2sim](https://huggingface.co/datasets/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**

```bash
# 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`](examples/agent_rl_research_lead.py).

```python
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:

```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`](https://github.com/cagataycali/robots/blob/feat/isaaclab-trainer/docs/learn/training/isaaclab.md) · PR: [strands-labs/robots#4227](https://github.com/strands-labs/robots/pull/4227).

**2 · Record** with strands `DatasetRecorder`

Recorded with [`examples/record_trained_policy.py`](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`.

```bash
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

```python
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)
```

```python
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):

```python
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`](https://github.com/cagataycali/robots/commit/fa66fc682047f3cb70e2e2bc60f0b2dde2fbebae) — [strands-labs/robots#4227](https://github.com/strands-labs/robots/pull/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`](LICENSE.md).