Isaac-GR00T/examples/SimplerEnv/README.md

# SimplerEnv

Framework for evaluating real-world robot manipulation policies (RT-1, RT-1-X, Octo) in simulation. Replicates common setups like Google Robot and WidowX+Bridge, with GPU-accelerated simulations (10-15x speedup). Offers visual matching and variant aggregation evaluation methods for robust policy assessment.

For more information, see the [official repository](https://github.com/simpler-env/SimplerEnv).

---

# Benchmark results

These values come from the default SimplerEnv evaluation runs (the per-task success rates are listed below).

## Bridge (WidowX robot)

Provided checkpoints:
- [nvidia/GR00T-N1.6-bridge](https://huggingface.co/nvidia/GR00T-N1.6-bridge)
- [nvidia/GR00T-N1.7-SimplerEnv-Bridge](https://huggingface.co/nvidia/GR00T-N1.7-SimplerEnv-Bridge)

| Task | N1.6 success rate | N1.7 success rate |
| --- | ---: | ---: |
| `widowx_spoon_on_towel` | 56/101 (55.4%) | 78/100 (78.0%) |
| `widowx_carrot_on_plate` | 46/100 (46.0%) | 58/100 (58.0%) |
| `widowx_put_eggplant_in_basket` | 89/100 (89.0%) | 53/100 (53.0%) |
| `widowx_stack_cube` | 5/100 (5.0%) | 48/100 (48.0%) |
| `widowx_put_eggplant_in_sink` | 33/100 (33.0%) | 2/100 (2.0%) |
| `widowx_close_drawer` | 73/100 (73.0%) | 97/100 (97.0%) |
| `widowx_open_drawer` | 95/100 (95.0%) | 100/100 (100.0%) |
| **Average** | **56.6%** | **62.3%** |

## Fractal (Google Robot)

Provided checkpoints:
- [nvidia/GR00T-N1.6-fractal](https://huggingface.co/nvidia/GR00T-N1.6-fractal)
- [nvidia/GR00T-N1.7-SimplerEnv-Fractal](https://huggingface.co/nvidia/GR00T-N1.7-SimplerEnv-Fractal)

| Task | N1.6 success rate | N1.7 success rate |
| --- | ---: | ---: |
| `google_robot_pick_coke_can` | 95/100 (95.0%) | 100/100 (100.0%) |
| `google_robot_pick_object` | 87/100 (87.0%) | 94/100 (94.0%) |
| `google_robot_move_near` | 81/100 (81.0%) | 100/100 (100.0%) |
| `google_robot_open_drawer` | 0/100 (0.0%) | 65/100 (65.0%) |
| `google_robot_close_drawer` | 44/100 (44.0%) | 69/100 (69.0%) |
| `google_robot_place_in_closed_drawer` | 5/100 (5.0%) | 7/100 (7.0%) |
| **Average** | **52.0%** | **72.5%** |

# Fine-tune Simpler Env bridge dataset (WidowX robot)

To reproduce our finetune results, use the following commands to setup dataset and launch finetune experiments. Please remember to set `WANDB_API_KEY` since W&B logging is on by default (`USE_WANDB=1` in `examples/finetune.sh`). If you don't have a WANDB account, prepend `USE_WANDB=0` to the launch command to disable it:

```bash
uv run hf download \
    --repo-type dataset IPEC-COMMUNITY/bridge_orig_lerobot \
    --local-dir examples/SimplerEnv/bridge_orig_lerobot/

# Copy the patches and run the finetune script
cp examples/SimplerEnv/bridge_modality.json examples/SimplerEnv/bridge_orig_lerobot/meta/modality.json
```

```bash
NUM_GPUS=8 MAX_STEPS=20000 GLOBAL_BATCH_SIZE=1024 SAVE_STEPS=1000 uv run bash examples/finetune.sh \
    --base-model-path nvidia/GR00T-N1.7-3B \
    --dataset-path examples/SimplerEnv/bridge_orig_lerobot/ \
    --embodiment-tag SIMPLER_ENV_WIDOWX \
    --output-dir /tmp/bridge_finetune \
    --state-dropout-prob 0.8
```

# Fine-tune Simpler Env fractal dataset (Google robot)

```bash
uv run hf download \
    --repo-type dataset IPEC-COMMUNITY/fractal20220817_data_lerobot \
    --local-dir examples/SimplerEnv/fractal20220817_data_lerobot/

# Copy the patches and run the finetune script
cp -r examples/SimplerEnv/fractal_modality.json examples/SimplerEnv/fractal20220817_data_lerobot/meta/modality.json
uv run python examples/SimplerEnv/convert_av1_to_h264.py --root examples/SimplerEnv/fractal20220817_data_lerobot --jobs 16
```

```bash
NUM_GPUS=8 MAX_STEPS=20000 GLOBAL_BATCH_SIZE=1024 SAVE_STEPS=1000 uv run bash examples/finetune.sh \
    --base-model-path nvidia/GR00T-N1.7-3B \
    --dataset-path examples/SimplerEnv/fractal20220817_data_lerobot/ \
    --embodiment-tag SIMPLER_ENV_GOOGLE \
    --output-dir /tmp/fractal_finetune \
    --state-dropout-prob 0.5
```

# Evaluate checkpoint

First, complete the [one-time simulation environment setup](../../README.md#one-time-simulation-environment-setup), then run this benchmark's setup script (only needed once per benchmark):

```bash
bash gr00t/eval/sim/SimplerEnv/setup_SimplerEnv.sh
```

Then, run client server evaluation under the project root directory in separate terminals:

## Fractal (Google Robot) Evaluation

**Terminal 1 - Server:**

You can use either a local finetuned checkpoint path or the remote finetuned checkpoint (provided by us):

**Option 1: Local finetuned checkpoint**
```bash
uv run python gr00t/eval/run_gr00t_server.py \
    --model-path /tmp/fractal_finetune/checkpoint-30000 \
    --embodiment-tag SIMPLER_ENV_GOOGLE \
    --use-sim-policy-wrapper
```

**Option 2: Remote finetuned checkpoint (directly runnable)**
```bash
uv run python gr00t/eval/run_gr00t_server.py \
    --model-path nvidia/GR00T-N1.7-SimplerEnv-Fractal \
    --embodiment-tag SIMPLER_ENV_GOOGLE \
    --use-sim-policy-wrapper
```

**Terminal 2 - Client:**
```bash
gr00t/eval/sim/SimplerEnv/simpler_uv/.venv/bin/python gr00t/eval/rollout_policy.py \
    --n-episodes 10 \
    --policy-client-host 127.0.0.1 \
    --policy-client-port 5555 \
    --max-episode-steps 300 \
    --env-name simpler_env_google/google_robot_pick_coke_can \
    --n-action-steps 1 \
    --n-envs 5
```

## Bridge (WidowX) Evaluation

**Terminal 1 - Server:**

**Option 1: Local finetuned checkpoint**
```bash
uv run python gr00t/eval/run_gr00t_server.py \
    --model-path /tmp/bridge_finetune/checkpoint-30000 \
    --embodiment-tag SIMPLER_ENV_WIDOWX \
    --use-sim-policy-wrapper
```

**Option 2: Remote finetuned checkpoint (directly runnable)**
```bash
uv run python gr00t/eval/run_gr00t_server.py \
    --model-path nvidia/GR00T-N1.7-SimplerEnv-Bridge \
    --embodiment-tag SIMPLER_ENV_WIDOWX \
    --use-sim-policy-wrapper
```

**Terminal 2 - Client:**
```bash
gr00t/eval/sim/SimplerEnv/simpler_uv/.venv/bin/python gr00t/eval/rollout_policy.py \
    --n-episodes 10 \
    --policy-client-host 127.0.0.1 \
    --policy-client-port 5555 \
    --max-episode-steps 300 \
    --env-name simpler_env_widowx/widowx_spoon_on_towel \
    --n-action-steps 4 \
    --n-envs 5
```

Other supported tasks are: 
```
simpler_env_google/google_robot_pick_object
simpler_env_google/google_robot_move_near
simpler_env_google/google_robot_open_drawer
...
simpler_env_widowx/widowx_spoon_on_towel
simpler_env_widowx/widowx_carrot_on_plate
simpler_env_widowx/widowx_stack_cube
```

you can replace the env_name with the corresponding tasks listed in the SimplerEnv fork this repo pins at `external_dependencies/SimplerEnv` (see `.gitmodules`).