kinder-openpi-checkpoints

Pi0.5 Fine-tuned Checkpoints for KinDER Benchmark

Fine-tuned Pi0.5 (Physical Intelligence) checkpoints for the KinDER benchmark tasks, trained using the kinder-openpi framework.

Model Description

These checkpoints are fine-tuned versions of Pi0.5, a Vision-Language-Action (VLA) model, adapted for various robotic manipulation tasks in the KinDER benchmark. The models use chain-of-thought (CoT) reasoning for improved task performance.

Available Checkpoints

3D Environment Tasks

2D Environment Tasks

Usage

Installation

Clone the kinder-openpi repository with submodules:

git clone --recurse-submodules [email protected]/Princeton-Robot-Planning-and-Learning/kinder-openpi/
cd kinder-openpi

Install dependencies using uv:

GIT_LFS_SKIP_SMUDGE=1 uv sync
GIT_LFS_SKIP_SMUDGE=1 uv pip install -e .

Inference

Step 1: Launch the Policy Server

uv run scripts/serve_policy.py policy:checkpoint \
  --policy.config=pi05_kinder_finetune \
  --policy.dir=checkpoints/<task_name>/<step>

Replace <task_name> with one of the available tasks (e.g., basemotion3d, motion2d) and <step> with the checkpoint step (e.g., 15000).

Step 2: Run Evaluation

Install the client:

pip install openpi_client tyro

Run evaluation:

# For 3D environments
python scripts/eval.py --use_overview_image --open-loop-horizon=8

# For 2D environments
python scripts/eval.py --no-use_overview_image

Training Details

• Base Model: Pi0.5 (Physical Intelligence)
• Training Framework: kinder-openpi
• Training Infrastructure: Multi-host TPU with FSDP sharding
• Data Format: RLDS (TensorFlow Datasets)

---

Citation

If you use these models, please cite the paper: KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning:

@inproceedings{huang2026kinder,
  title     = {KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning},
  author    = {Huang, Yixuan and Li, Bowen and Saxena, Vaibhav and Liang, Yichao and Mishra, Utkarsh and Ji, Liang and Zha, Lihan and Wu, Jimmy and Kumar, Nishanth and Scherer, Sebastian and Xu, Danfei and Silver, Tom},
  booktitle = {Robotics: Science and Systems (RSS)},
  year      = {2026}
}