kinder-DPES-checkpoints

KinDER — Diffusion Policy + Environment States (DPES) Checkpoints

Trained DP + Environment States (DPES) checkpoints for the KinDER physical-reasoning benchmark (RSS 2026).

Each checkpoint is an imitation learning policy trained from ~100 human demonstrations per environment. The training code lives in kinder-diffusion-policy (a fork of diffusion_policy). Demonstrations are available at kinder-bench/kinder-datasets.

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Checkpoints

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Method

DP + Environment States (DPES) extends standard Diffusion Policy by incorporating additional low-level environment state vectors as input alongside RGB images. The environment states are encoded with MLPs before being fused with the image features and passed to the diffusion model.

Comparison with Diffusion Policy (DP)

Inputs

2D environments — single overhead RGB image (224 × 224) + flat state vector:

3D TidyBot environments — three RGB cameras (base 224 × 224, wrist 224 × 224, overview 224 × 224) + flat state vectors as above.

The environment and robot state vectors are each passed through a small MLP encoder before concatenation with the visual features, giving the policy direct access to precise geometric information that may be hard to extract from pixels alone.

Output

Action chunk predicted by iterative DDPM denoising, identical to DP.

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Usage

1. Install dependencies

# Clone and set up kinder-diffusion-policy
git clone [email protected]:Princeton-Robot-Planning-and-Learning/kinder-diffusion-policy.git
cd kinder-diffusion-policy
# Follow the environment setup instructions in the repo README
mamba activate robodiff

# Install the kinder-imitation-learning inference utilities
cd kinder-baselines/kinder-imitation-learning
uv pip install -r prpl_requirements.txt
uv pip install -e ".[develop]"

2. Launch the policy server

cd ~/kinder-diffusion-policy
mamba activate robodiff
python policy_server.py --ckpt-path /path/to/sweep3d/epoch=0300-train_loss=0.001.ckpt

3. Run evaluation

cd kinder-baselines/kinder-models/scripts
python inference.py \
    --env-name kinder/SweepIntoDrawer3D-o5-v0 \
    --save-videos \
    --num-seeds 1 \
    --num-episodes 5 \
    --max-steps 200

Replace --env-name and --ckpt-path with the environment and checkpoint of your choice.

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Training from scratch

# Convert raw teleoperation recordings to HDF5
cd kinder-baselines/kinder-models/scripts
python demos_to_hdf5.py \
    --teleop_data_dir $YOUR_DATA_DIR \
    --output_path $OUTPUT_HDF5_PATH \
    --render_images

# Train with the DPES config (includes state inputs)
cd ~/kinder-diffusion-policy
mamba activate robodiff
python train.py --config-name=train_sweep3d_image_state

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Related resources

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Citation

If you use these datasets, 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}
}