KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning

• URL: http://arxiv.org/abs/2604.25788v2
• Date: Mon, 04 May 2026 04:14:46 GMT
• Title: KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
• Abstract summary: We introduce KinDER, a benchmark for Kinematic and Dynamic Embodied Reasoning.<n>KinDER comprises 25 procedurally generated environments, a Gymnasium-compatible Python library with parameterized skills and demonstrations.<n>We additionally include real-to-sim-to-real experiments on a mobile manipulator to assess the correspondence between simulation and real-world physical interaction.
• Score: 21.424432912557293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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/

Related papers

• Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning [128.76135832773102]
  Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world.<n>Recent robot learning methods have made substantial progress by integrating force, tactile, vision, language, and proprioceptive sensing into learned manipulation policies.<n>Despite these advances, existing surveys have not explicitly reviewed force- and tactile-aware robot learning from a unified perspective.
  arXiv  Detail & Related papers  (2026-08-02T14:22:01Z)
• IMBench: A Benchmark for Intuitive Robotic Manipulation [11.843368723732679]
  Humans combine reasoning and motor control to solve complex manipulation tasks under diverse constraints.<n>Existing benchmarks fail to capture this integration.<n>We introduce IMBENCH, a benchmark designed to evaluate intuitive manipulation as an integrated capability spanning perception, physical reasoning, action generation, and iterative execution.
  arXiv  Detail & Related papers  (2026-07-17T05:34:29Z)
• InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills [16.31202379413011]
  We develop a unified physics-based imitation learning framework for Real-world human-object Interaction (HOI) control.<n>InterReal enables humanoid robots to track HOI reference motions, facilitating the learning of fine-grained interactive skills.<n>A meta-policy guided by critical tracking error metrics explores and allocates reward signals to the low-level reinforcement learning objective.
  arXiv  Detail & Related papers  (2026-03-08T07:44:35Z)
• From Perception to Action: An Interactive Benchmark for Vision Reasoning [51.11355591375073]
  Causal Hierarchy of Actions and Interactions (CHAIN) benchmark designed to evaluate whether models can understand, plan, and execute structured action sequences grounded in physical constraints.<n> CHAIN shifts evaluation from passive perception to active problem solving, spanning tasks such as interlocking mechanical puzzles and 3D stacking and packing.<n>Our results show that top-performing models still struggle to internalize physical structure and causal constraints, often failing to produce reliable long-horizon plans and cannot robustly translate perceived structure into effective actions.
  arXiv  Detail & Related papers  (2026-02-24T15:33:02Z)
• HAIC: Humanoid Agile Object Interaction Control via Dynamics-Aware World Model [56.4392302336014]
  We present HAIC, a framework for robust interaction across diverse object dynamics without external state estimation.<n>Our key contribution is a dynamics predictor that estimates high-order object states (velocity, acceleration) solely from proprioceptive history.<n> Experiments on a humanoid robot show HAIC achieves high success rates in agile tasks.
  arXiv  Detail & Related papers  (2026-02-12T09:34:35Z)
• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects [59.51185639557874]
  We introduce Kinematify, an automated framework that synthesizes articulated objects directly from arbitrary RGB images or textual descriptions.<n>Our method addresses two core challenges: (i) inferring kinematic topologies for high-DoF objects and (ii) estimating joint parameters from static geometry.
  arXiv  Detail & Related papers  (2025-11-03T07:21:42Z)
• PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System [67.2851799763138]
  PhysHSI comprises a simulation training pipeline and a real-world deployment system.<n>In simulation, we adopt adversarial motion prior-based policy learning to imitate natural humanoid-scene interaction data.<n>For real-world deployment, we introduce a coarse-to-fine object localization module that combines LiDAR and camera inputs.
  arXiv  Detail & Related papers  (2025-10-13T07:11:37Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• Human-Object Interaction from Human-Level Instructions [17.10279738828331]
  We propose the first complete system for synthesizing human-object interactions for object manipulation in contextual environments.<n>We leverage large language models (LLMs) to interpret the input instructions into detailed execution plans.<n>Unlike prior work, our system is capable of generating detailed finger-object interactions, in seamless coordination with full-body movements.
  arXiv  Detail & Related papers  (2024-06-25T17:46:28Z)
• Closed Loop Interactive Embodied Reasoning for Robot Manipulation [17.732550906162192]
  Embodied reasoning systems integrate robotic hardware and cognitive processes to perform complex tasks.<n>We introduce a new modular Closed Loop Interactive Embodied Reasoning (CLIER) approach.<n>CLIER takes into account the measurements of non-visual object properties, changes in the scene caused by external disturbances as well as uncertain outcomes of robotic actions.
  arXiv  Detail & Related papers  (2024-04-23T16:33:28Z)
• Learning Extrinsic Dexterity with Parameterized Manipulation Primitives [8.7221770019454]
  We learn a sequence of actions that utilize the environment to change the object's pose. Our approach can control the object's state through exploiting interactions between the object, the gripper, and the environment. We evaluate our approach on picking box-shaped objects of various weight, shape, and friction properties from a constrained table-top workspace.
  arXiv  Detail & Related papers  (2023-10-26T21:28:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.