KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
Yixuan Huang, Bowen Li, Vaibhav Saxena, Yichao Liang, Utkarsh Aashu Mishra, Liang Ji, Lihan Zha, Jimmy Wu, Nishanth Kumar, Sebastian Scherer, Danfei Xu, Tom Silver

TL;DR
KinDER is a comprehensive benchmark with environments and tools designed to evaluate and advance physical reasoning capabilities in robot learning and planning, highlighting current method limitations.
Contribution
Introduces KinDER, a new benchmark with environments, evaluation suite, and baseline methods for physical reasoning in robotics, fully open-sourced.
Findings
Existing methods struggle with many KinDER environments.
KinDER isolates core physical reasoning challenges.
Real-to-sim-to-real experiments assess simulation fidelity.
Abstract
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 13 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…
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