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

Task	Description
`basemotion3d`	Basic 3D motion primitives
`shelf_3d`	Shelf manipulation in 3D
`sweep_3d`	Sweeping task in 3D environment
`transport3d`	Object transport in 3D

2D Environment Tasks

Task	Description
`motion2d`	Basic 2D motion primitives
`stickbutton2d`	Button pressing with stick in 2D
`dynobstruction2d`	Dynamic obstruction avoidance in 2D
`dynpushpullhook2d`	Push/pull with hook in dynamic 2D environment

Usage

Installation

Clone the kinder-openpi repository with submodules:

bash

git clone --recurse-submodules git@github.com/Princeton-Robot-Planning-and-Learning/kinder-openpi/
cd kinder-openpi

Install dependencies using uv:

bash

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

Inference

Step 1: Launch the Policy Server

bash

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:

bash

# 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:

bibtex

@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}
}

kinder-bench/kinder-openpi-checkpoints

Pi0.5 Fine-tuned Checkpoints for KinDER Benchmark

Model Description

Available Checkpoints

3D Environment Tasks

2D Environment Tasks

Usage

Installation

Inference

Step 1: Launch the Policy Server

Step 2: Run Evaluation

Training Details

Citation

kinder-bench/
kinder-openpi-checkpoints