KinDER: A Physical Reasoning Benchmark
for Robot Learning and Planning

Yixuan Huang* Princeton
Bowen Li* CMU
Vaibhav Saxena* Georgia Tech
Yichao Liang Cambridge
Utkarsh Mishra Georgia Tech
Liang Ji Princeton
Lihan Zha Princeton
Jimmy Wu NVIDIA
Nishanth Kumar MIT
Sebastian Scherer CMU
Danfei Xu Georgia Tech, NVIDIA
Tom Silver Princeton

*Equal contribution

Hover over the environments below to see them in action

BalanceBeam3D Demo BalanceBeam3D BaseMotion3D Demo BaseMotion3D ClutteredRetrieval2D Demo ClutteredRetrieval2D ClutteredStorage2D Demo ClutteredStorage2D ConstrainedCupboard3D Demo ConstrainedCupboard3D DynObstruction2D Demo DynObstruction2D DynPushPullHook2D Demo DynPushPullHook2D DynPushT2D Demo DynPushT2D DynScoopPour2D Demo DynScoopPour2D Dynamo3D Demo Dynamo3D Motion2D Demo Motion2D Obstruction2D Demo Obstruction2D Obstruction3D Demo Obstruction3D Packing3D Demo Packing3D PushPullHook2D Demo PushPullHook2D Rearrange3D Demo Rearrange3D ScoopPour3D Demo ScoopPour3D Shelf3D Demo Shelf3D SortClutteredBlocks3D Demo SortClutteredBlocks3D StickButton2D Demo StickButton2D SweepIntoDrawer3D Demo SweepIntoDrawer3D SweepSimple3D Demo SweepSimple3D Table3D Demo Table3D Tossing3D Demo Tossing3D Transport3D Demo Transport3D

About KinDER

A physical reasoning benchmark for robot learning and planning.

Robotic systems that interact with the physical world must reason about kinematic and dynamic constraints imposed by their own embodiment, their environment, and the task at hand. We introduce KinDER, a benchmark for Kinematic and Dynamic Embodied Reasoning that targets physical reasoning challenges arising in robot learning and planning. KinDER comprises 25 procedurally generated environments, a Gymnasium-compatible Python library with parameterized skills and demonstrations, and a standardized evaluation suite with 8 implemented baselines spanning task and motion planning, imitation learning, reinforcement learning, and foundation-model-based approaches. The environments are designed to isolate five core physical reasoning challenges: basic spatial relations, nonprehensile multi-object manipulation, tool use, combinatorial geometric constraints, and dynamic constraints, disentangled from perception, language understanding, and application-specific complexity. Empirical evaluation shows that existing methods struggle to solve many of the environments, indicating substantial gaps in current approaches to physical reasoning. We additionally include real-to-sim-to-real experiments on a mobile manipulator to assess the correspondence between simulation and real-world physical interaction. KinDER is fully open-sourced and intended to enable systematic comparison across diverse paradigms for advancing physical reasoning in robotics.

What Makes KinDER Challenging? ▼

For Reinforcement Learning

Environments have long horizons and sparse rewards. Users are welcome to engineer dense rewards, but doing so may be nontrivial. Environments also have very diverse task distributions, so learned policies must generalize.

For Imitation Learning

Physical reasoning requires understanding physical constraints (e.g., spatial relationships, kinematics, dynamics). Imitating surface-level patterns in demonstrations is not enough to generalize to broad task distributions.

For Vision-Language Models

The physical reasoning required in KinDER is not easy to represent in natural language. Spatial reasonining, a subset of physical reasoning, is a known challenge for vision-language models.

For Hierarchical Approaches

Approaches that first decide "what to do" and then decide "how to do it" -- whether in hierarchical RL, planning, or with multi-level foundation models -- will run into difficulties when there are couplings between these high-level and low-level decisions.

For Task and Motion Planning

KinDER does not provide any models for TAMP. Users are welcome to engineer their own, but doing so may be nontrivial. Some environments contain many objects, which may make planning slow.

For Human Engineers

KinDER features diverse task distributions and long time horizons, making it challenging for engineers to hand-design even very environment-specific solutions. A single task may be straightforward, but designing generalized solutions is nontrivial.

Installation and Usage

Installation

pip install kindergarden          # Core dependencies only
pip install kindergarden[all]     # All environment dependencies

# Or install specific environment categories
pip install kindergarden[kinematic2d]
pip install kindergarden[dynamic2d]
pip install kindergarden[kinematic3d]
pip install kindergarden[dynamic3d]

For development or installing from source, see the GitHub repository.

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:

from kinder.envs.geom2d.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, observations are provided as vectors. Convert between vectors and object-centric states:

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)

Baselines

We provide implementations of several baselines in the kinder-baselines repository. Installation and usage instructions are available there.

Bilevel Planning

TAMP-style bilevel planning

Domain-Specific Policies

Hand-engineered policies with domain-specific models

Diffusion Policies

Learning from demonstrations

Reinforcement Learning

RL with sparse and shaped rewards

VLA Policies

Finetuning pi-0.5 with demonstrations

LLM & VLM Planning

Large language and vision-language model based planning

Tutorials

Step-by-step tutorials for getting started with KinDER.

kinder

Getting Started

Walk through the basics: discovering environments, creating one, taking actions, and rendering a video.
kinder

Model-Predictive Control

Use get_state, set_state, and get_next_state for model-based planning with random-shooting MPC.
kinder-bilevel-planning

Bilevel Planning

Set up predicates, operators, and skills, then run a SESAME-style bilevel planner on an Obstruction2D task.

KinDERGarden: Environments

KinDER environments are organized into four categories.

Kinematic 2D

Kinematic 3D

Dynamic 2D

Dynamic 3D

KinderBench: Benchmark and Results

See the plot below for a summary of our main empirical results. See paper for details and additional results with additional metrics.

Benchmark results

Success rates: mean ± std across 5 seeds and 50 episodes per seed.

Real Robot Validation

Real robot versions of KinDER tasks with real-to-sim-to-real planning.