KinDER

KinDER: A physical reasoning benchmark for robot learning and planning.
Robotics: Science and Systems (RSS), 2026

Website

See https://prpl-group.com/kinder-site/ for documentation and tutorials.

Requirements

1. Python >=3.10, <3.13
2. Tested on MacOS 13-15, Ubuntu 20.04, Ubuntu 22.04, Ubuntu 24.04, and Windows 10 (but we aim to support most platforms)

Installation

From PyPI

pip install kindergarden   # all environments (PyBullet, MuJoCo, pygame, ...)

Installing a single backend (no PyBullet/MuJoCo)

The default install pulls every backend. To install just one — for example the kinematic2d environments, which need neither PyBullet nor MuJoCo — install the package without its dependencies, then the backend's requirements file:

pip install --no-deps kindergarden
pip install -r https://raw.githubusercontent.com/Princeton-Robot-Planning-and-Learning/kindergarden/main/requirements/kinematic2d.txt

Requirements files are provided for each backend: kinematic2d, dynamic2d, kinematic3d, dynamic3d (under requirements/). The two-step form is needed because pip extras can only add dependencies, never remove the backends already pulled in by the base install.

From Source

We strongly recommend uv, but other standard setups work too.

git clone https://github.com/Princeton-Robot-Planning-and-Learning/kindergarden.git
cd kindergarden
uv pip install -e ".[develop]"   # all dependencies + dev tools

Or install only one backend (no PyBullet/MuJoCo):

uv pip install --no-deps -e .
uv pip install -r requirements/kinematic2d.txt

To check the installation, run ./run_ci_checks.sh. It should complete with all green successes.

Usage Example

Basic Usage (Gym API)

import kinder
kinder.register_all_environments()
env = kinder.make("kinder/Obstruction2D-o3-v0")  # 3 obstructions
obs, info = env.reset()  # procedural generation
action = env.action_space.sample()
next_obs, reward, terminated, truncated, info = env.step(action)
img = env.render()  

Object-Centric States

All environments in KinDER use object-centric states. For example:

from kinder.envs.kinematic2d.obstruction2d import ObjectCentricObstruction2DEnv
env = ObjectCentricObstruction2DEnv(num_obstructions=3)
obs, _ = env.reset(seed=123)
print(obs.pretty_str())

Here, obs is an ObjectCentricState, and the printout is:

############################################################### STATE ###############################################################
type: crv_robot           x         y    theta    base_radius    arm_joint    arm_length    vacuum    gripper_height    gripper_width
-----------------  --------  --------  -------  -------------  -----------  ------------  --------  ----------------  ---------------
robot              0.885039  0.803795  -1.5708            0.1          0.1           0.2         0              0.07             0.01

type: rectangle           x         y    theta    static    color_r    color_g    color_b    z_order      width     height
-----------------  --------  --------  -------  --------  ---------  ---------  ---------  ---------  ---------  ---------
obstruction0       0.422462  0.100001        0         0       0.75        0.1        0.1        100  0.132224   0.0766399
obstruction1       0.804663  0.100001        0         0       0.75        0.1        0.1        100  0.0805652  0.0955062
obstruction2       0.559246  0.100001        0         0       0.75        0.1        0.1        100  0.12608    0.180172

type: target_block          x         y    theta    static    color_r    color_g    color_b    z_order     width    height
--------------------  -------  --------  -------  --------  ---------  ---------  ---------  ---------  --------  --------
target_block          1.20082  0.100001        0         0   0.501961          0   0.501961        100  0.138302  0.155183

type: target_surface           x    y    theta    static    color_r    color_g    color_b    z_order     width    height
----------------------  --------  ---  -------  --------  ---------  ---------  ---------  ---------  --------  --------
target_surface          0.499675    0        0         1   0.501961          0   0.501961        101  0.180286       0.1
#####################################################################################################################################

For compatibility with baselines, the observations provided by the main environments are vectors. It is easy to convert between vectors and object-centric states. For example:

import kinder
kinder.register_all_environments()
env = kinder.make("kinder/Obstruction2D-o3-v0")
vec_obs, _ = env.reset(seed=123)
object_centric_obs = env.observation_space.devectorize(vec_obs)
recovered_vec_obs = env.observation_space.vectorize(object_centric_obs)

Noisy Observation and Action Wrappers

KinDER provides Gymnasium-compatible wrappers for adding stochasticity to observations and actions:

import kinder
kinder.register_all_environments()
env = kinder.make("kinder/Obstruction2D-o3-v0")
env = kinder.NoisyObservation(env, noise_std=0.05)  # Gaussian noise on observations
env = kinder.NoisyAction(env, noise_std=0.01)        # Gaussian noise on actions (clipped to bounds)
obs, info = env.reset(seed=42)

noise_std can be a scalar (uniform across dimensions) or a per-dimension array. NoisyAction automatically clips noisy actions to the action space bounds.

Quick Environment Reference

Hugging Face Models and Datasets

Pre-trained model checkpoints and demonstration datasets are available on Hugging Face:

• Models: https://huggingface.co/kinder-bench
• Dataset: https://huggingface.co/datasets/kinder-bench/kinder-datasets

Acknowledgements

We thank the authors of following projects for open-sourcing their code, whose assets we utilized in KinDER:

• RoboCasa - for object assets in Dynamic3D envs
• MimicLabs - for scene assets in Dynamic3D envs

Contributing

General Guidelines

• All checks must pass before code is merged (see ./run_ci_checks.sh)
• All code goes through the pull request review process

Adding New Environments

Some new environment requests are in Issues. To add a new environment, please see the examples in src/kinder/env. Also consider:

• Environments are registered in src/kinder/__init__.py
• Each environment should have at least one demonstration (see scripts/collect_demos.py)
• After collecting a demonstration, create a video with scripts/generate_demo_video.py, which will be used in the autogenerated documentation