KinDER：面向机器人学习与规划的物理推理基准测试平台

摘要

与物理世界交互的机器人系统必须对其自身结构、所处环境及当前任务所施加的运动学和动力学约束进行推理。我们推出KinDER——一个面向运动学与动力学具身推理的基准测试平台，旨在解决机器人学习与规划中出现的物理推理难题。该平台包含25个程序化生成的环境、兼容Gymnasium的Python库（内含参数化技能与演示）、以及配备13个已实现基线的标准化评估套件，涵盖任务与运动规划、模仿学习、强化学习和基于基础模型的方法。这些环境专门设计用于隔离五大核心物理推理挑战：基础空间关系、非抓取式多物体操控、工具使用、组合几何约束及动态约束，使其独立于感知、语言理解和特定应用复杂度。实证评估表明，现有方法在多数环境中表现不佳，揭示了当前物理推理方法存在的显著不足。我们还通过移动机械臂的实景-仿真-实景实验验证仿真与真实世界物理交互的对应关系。KinDER完全开源，旨在推动机器人物理推理研究实现跨范式的系统性比较。项目网站与代码：https://prpl-group.com/kinder-site/

English

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 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. Website and code: https://prpl-group.com/kinder-site/

KinDER：面向机器人学习与规划的物理推理基准测试平台

KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning

摘要

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