KinDER为机器人物理推理提供可复现的基准测试，涵盖五类核心挑战。
KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
- 构建25个程序生成环境，分离出五类物理推理任务
- 现有方法在多数环境中表现不佳，暴露能力短板
- 支持仿真与真实机器人对比实验，适合机器人学习研究者
机器人在与物理世界交互时需考虑自身形态、环境及任务带来的运动学和动力学约束。我们提出KinDER，一个针对运动学与动力学具身推理的基准，聚焦机器人学习与规划中的物理推理挑战。KinDER包含25个程序生成环境、兼容Gymnasium的Python库（含参数化技能与示范），以及涵盖13种基线方法的标准化评估套件，覆盖任务规划、运动规划、模仿学习、强化学习和基础模型方法。环境设计旨在分离出五大核心物理推理挑战：基本空间关系、非抓取多物体操作、工具使用、组合几何约束和动态约束，剥离感知、语言理解及应用特定复杂性。实证评估显示，现有方法在多数环境中难以解决，表明当前物理推理存在显著差距。我们还通过移动操作臂开展真实-仿真-真实实验，验证仿真与现实物理交互的一致性。KinDER完全开源，旨在推动不同范式下机器人物理推理的系统性比较与进步。官网与代码：https://prpl-group.com/kinder-site/
原文摘要 · Abstract (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/
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