kinder-bench/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.

---

Checkpoints

Path	KinDER environment	Trained epochs	Final train loss
motion2d/epoch=1000-train_loss=0.000.ckpt	Motion2D-p0	1 000	0.000
stickbutton2d/epoch=2000-train_loss=0.001.ckpt	StickButton2D-b1	2 000	0.001
dynobstruction2d/epoch=2000-train_loss=0.000.ckpt	DynObstruction2D-o1	2 000	0.000
dynpushpullhook2d/epoch=0900-train_loss=0.001.ckpt	DynPushPullHook2D-o5	900	0.001
basemotion3d/epoch=2000-train_loss=0.000.ckpt	BaseMotion3D	2 000	0.000
shelf3d/epoch=0300-train_loss=0.000.ckpt	Shelf3D	300	0.000
sweep3d/epoch=0300-train_loss=0.001.ckpt	SweepIntoDrawer3D	300	0.001
transport3d/epoch=0100-train_loss=0.000.ckpt	Transport3D-o2	100	0.000

---

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)

	DP	DPES
RGB image input	✓	✓
Environment state input	—	✓ (MLP-encoded)
Robot state input	—	✓ (MLP-encoded)
Action output	diffusion	diffusion

Inputs

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

State vector	Content
robot_state	Robot proprioception (joint positions, velocities)
env_state	Object / scene state (positions, orientations of all relevant entities)

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.

---

Usage

1. Install dependencies

# Clone and set up kinder-diffusion-policy
git clone git@github.com: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.

---

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

---

Related resources

Resource	Link
KinDER benchmark	kindergarden
Training code	kinder-diffusion-policy
Demonstration datasets	kinder-bench/kinder-datasets
DP (image-only) checkpoints	kinder-bench/kinder-DP-checkpoints
Finetuned π0.5 VLA checkpoints	kinder-openpi

---

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}
}