PaperlayerView exact revision on arXiv

Source-linked AI summary

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

arXiv:2604.25788v2cs.RO

TL;DR

Robots must reason about constraints from their bodies, environments, and tasks, making physical reasoning a central challenge. KinDER benchmarks this reasoning across 25 environments and 13 baselines; BP achieved the highest average success rate (0.57).

  • Problem

    Robots must reason about kinematic and dynamic constraints imposed by their morphology, environment, and task requirements.

    Source: §I · p. 1

  • Method

    KinDER provides 25 procedurally generated environments and a standardized benchmark with 13 pre-implemented baselines spanning several robotics approaches.

    Source: §I · p. 2

  • Results

    Across the benchmark, BP obtained the highest average success rate (0.57) among the evaluated baselines.

    Source: §VI · p. 7

  • Takeaways & Limitations

    KinDER provides a standardized basis for comparing approaches to physical reasoning in robotics.

    Source: §I · p. 2

    The simulations do not fully capture real-world physics and interaction, and the benchmark excludes stochasticity, partial observability, diverse robot embodiments, and multi-robot coordination.

    Source: §VIII · p. 8

Abstract

from arXiv · show

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/

I. INTRODUCTION

KinDER targets physical reasoning about the kinematic and dynamic constraints imposed by a robot’s morphology, its environment, and task requirements. It provides procedurally varied environments, software and demonstrations, and a standardized benchmark for evaluating approaches to these challenges.

I. INTRODUCTION

  • Physical reasoning requires robots to account for constraints from their morphology, environment physics, and task requirements [5] [8].These constraints can turn apparently simple tasks into challenging puzzles.Source: §I · pp. 1–2
  • Robots must decide what to reason about from sensory observations, including whether obstacles, regrasping, or space constraints affect a task [13].The introduction illustrates this with storing a book on a shelf, which may require moving obstacles, setting down and regrasping the book, or pushing other books aside.Source: §I · p. 2
  • Existing benchmarks make targeted evaluation difficult because they focus on broader learning and planning challenges or application domains, rather than isolating physical reasoning from perception, language, and domain complexity.No single task or procedurally generated variations of one task capture physical reasoning in its full generality.Source: §I · p. 2
  • Physical reasoning is studied across TAMP with explicit models, RL and IL with data-driven policies, and foundation-model approaches combining language reasoning with pretrained understanding [36] [37] [38] [5] [43] [44] [45].The authors note that this diversity, alongside limited clarity on the state of the art, makes progress difficult.Source: §I · p. 2
  • KinDERGarden provides 25 simulated environments, each with infinite procedurally generated variations, to capture different facets of physical reasoning.The environments target basic spatial relations, nonprehensile multi-object manipulation, combinatorial geometric constraints, tool use, and dynamic constraints.Source: §I · p. 2, Figure 2 · p. 2
  • KinDERGym provides a Gymnasium-compatible API, parameterized skills and concepts, teleoperation interfaces, and demonstrations; KinDERBench standardizes evaluation with 13 TAMP, RL, IL, and FM baselines.The baselines span task and motion planning, reinforcement learning, imitation learning, and foundation-model approaches.Source: §I · p. 2
  • KinDER’s contributions are open-source, tested on multiple standard operating systems, and include real-to-sim-to-real results to assess correspondence between simulated and real physical reasoning challenges.The authors present the benchmark as a step toward clarifying and advancing the state of the art in robot physical reasoning.Source: §I · p. 2

II. KINDER CORE CHALLENGES

KinDER prioritizes physical reasoning challenges that are active research frontiers but are not well represented in existing benchmarks, where the state of the art remains unclear.

Basic Spatial Relations

  • Basic spatial relations require passive recognition of object arrangements and active ability to create the desired relations.The challenge includes both determining where objects are relative to one another and placing them accordingly.Source: §II · p. 3

Nonprehensile Multi-Object Manipulation

  • Nonprehensile multi-object manipulation extends beyond pick-and-place to simultaneous object handling and purposeful whole-arm or whole-body contact.Robots may need to push, pull, sweep, scoop, stir, or slap multiple objects while leveraging contact.Source: §II · p. 3

Tool Use

  • Tool use requires manipulating objects with other objects and understanding common affordances as well as abstract mechanisms for improvisation.The challenge spans both familiar tool functions and less conventional uses of objects.Source: §II · p. 3

Combinatorial Geometric Constraints

  • Combinatorial geometric constraints require avoiding object-object and robot-object collisions in tight, cluttered spaces, with constraint counts growing polynomially as objects increase.Packing, retrieval, and navigation can require satisfying many implicit geometric constraints.Source: §II · p. 3

Dynamic Constraints

  • Dynamic constraints require control that stabilizes physical systems while respecting limits such as safe velocities and task requirements like preventing spills.Examples include carrying a full cup, balancing a tray, and scooping and pouring without spilling.Source: §II · p. 3

Scope

  • The five challenges are not exhaustive; KinDER also involves long horizons, sparse goal-based feedback, broad task distributions, and time pressure, which are omitted to focus on physical reasoning.Progress on the selected challenges would nevertheless represent a significant step forward for the field.Source: §II · p. 3

III. RELATED WORK

KinDER focuses on core physical reasoning challenges rather than application-driven complexity, and includes 2D and 3D environments for studying reasoning at multiple abstraction levels. It complements existing benchmarks by enabling focused evaluation of physical reasoning in robotics.

a) Benchmarks for Robot Learning and Planning

  • Robot-learning benchmarks span imitation, reinforcement, and foundation-model methods, and KinDER especially draws inspiration from LIBERO and MimicLabs.Common benchmark challenges include long horizons, sparse rewards, language grounding, and diverse tasks, scenes, and objects.Source: §III · p. 3
  • KinDERGym provides parameterized skills and concepts, while KinDERBench compares planning, learning, and combined methods in a field with few combined task-and-motion-planning benchmarks.The authors identify Lagriffoul et al. as the only combined TAMP benchmark they know of, and say it is not actively used.Source: §III · p. 4

b) Application-Driven Benchmarks

  • Unlike application-driven benchmarks spanning home assistance, assembly, cleanup, and cooking, KinDER is designed to isolate and advance robot physical reasoning.Application-focused benchmarks include ALFRED, AI2-THOR [78], ManipulaTHOR, BEHAVIOR-1k [17], Habitat, ManiSkill-HAB, RoboCasa, FurnitureBench, CleanUpBench [81], and CookBench [82].Source: §III · p. 4

c) Physical Reasoning Benchmarks

  • Inspired by physical-reasoning benchmarks such as the Virtual Tools Game and PHYRE, KinDER targets robotics and emphasizes continuous spaces, long-horizon decisions, procedural generation, and kinematic and dynamic constraints.Connecting KinDER with human-like physical reasoning approaches is identified as a future opportunity.Source: §III · p. 4

IV. KINDERGARDEN: ENVIRONMENTS

KinDERGarden provides 25 procedurally generated robot physical-reasoning environments across four categories: Kinematic2D, Dynamic2D, Kinematic3D, and Dynamic3D. The categories vary in dimensionality, modeled dynamics, robot embodiment, and interaction mechanics.

General Environment Structure

  • All environments use Gymnasium's API, sparse success-only rewards, procedurally generated task distributions, and object-centric feature-vector states that support varying object counts.Baselines may also use RGB observations or fixed-object variants flattened to fixed-dimensional vectors for standard reinforcement and imitation learning.Source: §IV · p. 4

Kinematic2D Environments

  • Kinematic2D's six environments study tool use and geometric constraints with pose-only transitions, an extendable arm, and an activatable vacuum that attaches nearby objects.Actions make small configuration changes, and collision-producing transitions are reverted; the environments use no physics backend.Source: §IV · p. 4

Dynamic2D Environments

  • Dynamic2D's four environments model velocities and accelerations with a two-fingered gripper; unlike Kinematic2D, DynObstruction2D can push obstacles aside while holding the target.In Obstruction2D, obstacles must be picked and placed before the target; the dynamic version permits pushing them aside when space allows.Source: §IV · pp. 4–5, Figure 5 · p. 5

Kinematic3D Environments

  • Kinematic3D's five environments target spatial relations and geometric constraints, using PyBullet modeling and collision checks with a TidyBot++ base, 7DOF arm, and gripper.Transitions omit velocities and accelerations, use small configuration changes, and revert states when collisions occur.Source: §IV · p. 5

Dynamic3D Environments

  • Dynamic3D's 10 environments cover all five core challenges, model velocities and accelerations in MuJoCo, and use dynamic grasps that never rigidly attach objects to the robot.They share a TidyBot++ mobile manipulator and Python code, with environment configuration files specifying differences and procedural task generation.Source: §IV · p. 5

V. KINDERGYM: ACCESSIBLE SOFTWARE

KinDERGym is a pip-installable Python package for KinDERGarden that provides parameterized skills and concepts, teleoperation interfaces, and demonstrations. It supports planning and learning while providing tools for human demonstration collection.

V. KINDERGYM: ACCESSIBLE SOFTWARE

  • KinDERGym packages KinDERGarden access, parameterized skills and concepts, teleoperation interfaces, and demonstrations, with nearly 400 unit tests and support tested across Python 3.10–3.12, Ubuntu 20.04–24.04, macOS 12–15, and Windows 10.The package follows continuous integration, linting, type checking, and autoformatting practices.Source: §V · p. 5

Parameterized Skills and Concepts

  • Skills are options paired with PDDL operators and samplers, supporting object and additional parameters, internal state, and patterns such as generating and following motion plans.For example, Pick(object, θ) can represent different relative grasps θ ∈ SE(3).Source: §V · pp. 5–6
  • Concepts are relational predicates with classifiers grounded in object-centric states, used in skill preconditions and effects and interpretable with those states as a two-level scene graph.For example, On(object, surface) is true when the object is above and in contact with the surface.Source: §V · p. 6
  • KinDERGym skills and concepts support bilevel, LLM, and VLM planning baselines, while engineering or learning useful hierarchical task decompositions remains an open direction.Physical reasoning tasks do not always admit readily apparent or easy-to-engineer hierarchical decompositions.Source: §V · p. 6

Teleoperation Interfaces and Demonstrations

  • Kinematic2D and Dynamic2D support mouse-and-keyboard or PS5 control, while Kinematic3D and Dynamic3D support an iPhone web app based on TidyBot++ [89] or Meta Quest 3S, with base and task-space arm control.The interfaces enable human demonstration collection; the iPhone app uses gyroscope and accelerometer inputs, and inverse kinematics maps task-space arm inputs to environment actions.Source: §V · p. 6
  • KinDERGym combines teleoperation and planning to provide at least 100 precollected demonstrations for 10 environments, and encourages users to contribute additional demonstrations.Planners can derive demonstrations in environments with implemented skills and concepts.Source: §V · p. 6

VI. KINDERBENCH: BASELINES AND METRICS

KinDERBench evaluates 13 robot-learning and planning baselines across eight environments using effectiveness, efficiency, and engineering-cost measures. Bilevel Planning achieves the highest average success rate, while results also reveal gaps in in-context planning, learned dynamics, and long-horizon reinforcement learning.

a) Environments; b) Baselines; c) Evaluation Metrics

  • KinDERBench selects two difficulty levels from each of four environment categories and measures success rate, rewards on successful episodes, inference time, and engineering cost.The benchmark covers eight environments, including tool use, object transport, dynamic obstacles, and space-constrained packing; it evaluates 13 representative planning and learning baselines. Rewards are −1 until success, and inference time is per-episode wall-clock seconds; engineering cost captures the effort required to run a method in a new environment.Source: §VI · pp. 6–7

d) Benchmark Results

  • BP leads average success rate (0.57), followed by LLMCon and VLMCon (0.43 each); evaluation used 5 seeds and 50 episodes per seed.The remaining averages are LLMPlan (0.34), VLMPlan (0.34), MPC (0.32), VLA (0.32), GSC (0.26), DPES (0.25), DP (0.24), PPO (0.13), MBRL (0.08), and SAC (0.02). The reported trend is that higher engineering costs and inference time accompany higher success rates.Source: §VI · p. 7
  • In-context examples improve LLM/VLM planning performance, while similar LLMPlan and VLMPlan results suggest added images provide little benefit beyond object-centric states.Despite using the same parameterized skills, BP outperforms LLMPlan and VLMPlan, particularly in more challenging environments.Source: §VI · p. 7
  • VLA achieves 0.43 success on five-obstacle DynPushPullHook2D, while DP and DPES achieve 0.14 and 0.04 on SweepIntoDrawer3D, respectively.DynPushPullHook2D requires tool use and nonprehensile multi-object manipulation; SweepIntoDrawer3D requires opening a drawer, grasping a sweeper, and sweeping multiple objects inside. DPES performs comparably to DP despite receiving object-centric states.Source: §VI · p. 7
  • MPC performs well with sparse rewards, whereas MBRL performs worse using the same planner; PPO and SAC succeed mainly on short-horizon tasks and remain low overall.The authors attribute MPC's performance to predictive sampling and MBRL's weaker performance to an unreliable learned transition model; dense rewards improve RL results but success rates remain low.Source: §VI · p. 7

e) Additional Results

  • After training with one obstacle, DP and VLA perform surprisingly well when tested with zero, two, or three obstacles, though performance degrades; VLA is particularly robust.The authors suggest VLA's robustness may stem from pretraining; Bilevel Planning's success rate and planning time substantially decrease and increase, respectively, as objects increase in StickButton2D.Source: §VI · p. 7

VII. REAL WORLD VALIDATION

The section demonstrates real-to-sim-to-real validation using TidyBot++ [89] and KinDERGarden’s Shelf3D environment, transferring a simulation-generated plan to the real robot from estimated robot and object states.

VII. REAL WORLD VALIDATION

  • The example pairs TidyBot++ [89] with KinDERGarden’s Shelf3D simulator to examine correspondence between its physical reasoning challenges and the real world.The authors also present it as a starting point for future real-to-sim-to-real research using KinDER.Source: §VII · p. 7
  • Overhead-camera estimates of robot and object poses initialize corresponding simulator states, where a plan is generated and then executed on the real robot.Object bounding boxes and poses are obtained using.Source: Figure 6 · p. 7, §VII · p. 8

VIII. LIMITATIONS AND DISCUSSION · APPENDIX

KinDER’s authors note limits in simulation fidelity, benchmark scope, and baseline coverage, while committing to maintain the open-source benchmark as more methods are developed and evaluated.

VIII. LIMITATIONS AND DISCUSSION

  • The simulation does not fully capture real-world physics and interaction.The authors note that this limitation applies to simulation-based benchmarks generally.Source: §VIII · p. 8
  • KinDER targets mid-level reasoning challenges for which fine-grained physical details may be less critical.The authors distinguish these challenges from physical reasoning dimensions where real-world fidelity matters more.Source: §VIII · p. 8
  • The benchmark excludes stochasticity and partial observability to keep its scope manageable.Source: §VIII · p. 8
  • Its design also excludes diverse robot embodiments and multi-robot coordination.Source: §VIII · p. 8
  • The authors selected standard, broadly representative baselines, but did not evaluate many alternative approaches.Source: §VIII · p. 8
  • The authors will support the community and maintain KinDER open-source as researchers develop and evaluate more sophisticated methods.Source: §VIII · p. 8

A. KinDERGarden Environment Details

KinDERGarden’s environment details are organized across Tables IX–XII, which report state and action spaces for the environments’ variants. Variant state spaces can differ with the number of objects (N).

A. KinDERGarden Environment Details

B. KinDERGym Additional Details

KinDERGym demonstrations use different teleoperation schemes for 2D and 3D environments.

Teleoperation

  • 2D teleoperation uses a PS5 controller or keyboard for direct action control, while 3D modes separately control base, arm, and gripper via joint commands or arm end-effector poses mapped by inverse kinematics.Source: B. KinDERGym Additional Details · p. 14

C. KinDERBench Additional Details · D. Additional Real-to-Sim-to-Real Details

The additional benchmark details specify baseline implementations and show that RL performance in BaseMotion3D is sensitive to reward design. The real-to-sim-to-real experiments reconstruct object-centric states, plan with bilevel planning, and execute plans by achieving waypoints sequentially.

C. KinDERBench Additional Details

  • Bilevel Planning searches up to 10 abstract plans or 60 seconds, while zero-shot LLM/VLM and in-context planners generate skill plans from states, goals, and, for VLMs, images.LLMPlan and LLMCon use GPT-5.2 [100]; VLMPlan and VLMCon use GPT-5.2 [43] [100]. In-context variants receive up to four examples, depending on task.Source: C. KinDERBench Additional Details · p. 14
  • MPC [101] samples 10 noisy action sequences of horizon 100 per iteration, rolls them out in simulation, and executes the first action of the lowest-cost candidate.Trajectories are warm-started, use 10 control points for smoothness, and are scored with the sparse reward.Source: C. KinDERBench Additional Details · p. 14
  • MBRL learns a state-transition model from imitation demonstrations before using MPC, while Generative Skill Chaining composes probabilistic skill models into plans for unseen tasks.The transition model has separate output heads for robot-state and environment-state changes; the diffusion framework converts demonstrations to Ant-Maze format.Source: C. KinDERBench Additional Details · p. 14
  • PPO [36] and SAC [37] use four-layer tanh MLPs and train for 1M environment steps, with 128-unit layers in 2D and 256-unit layers in 3D.PPO uses a 0.2 clip ratio; SAC uses a 500,000-entry replay buffer and delays learning until 5,000 steps.Source: C. KinDERBench Additional Details · p. 14
  • Diffusion Policy predicts 16-step action trajectories, while DPES adds environment-state inputs and fine-tuned π0.5 uses the same state and action spaces without environment-state access.Diffusion Policy conditions on two observation steps and executes eight future actions; π0.5 executes the first eight actions of a 10-action horizon [104].Source: C. KinDERBench Additional Details · pp. 14–15
  • In BaseMotion3D, engineered dense rewards substantially improve PPO, while the same shaping reward decreases SAC performance.The reward measures changes in Euclidean distance to the goal projected onto the XOY plane; the comparison uses five random seeds.Source: Table VII · p. 15, C. KinDERBench Additional Details · pp. 15–16

D. Additional Real-to-Sim-to-Real Details

  • The real-to-sim pipeline estimates robot base pose from calibrated top-down cameras, obtains proprioception directly, and detects object bounding boxes to construct object-centric simulation states.Bounding-box centers estimate object positions, sizes represent scale, and the simulated z-axis dimension remains fixed.Source: D. Additional Real-to-Sim-to-Real… · p. 16
  • The team generates task and motion plans with bilevel planning, then executes them on the real robot by sequentially achieving each planned waypoint.These initial experiments are presented as a starting point for real-to-sim-to-real systems for more complex tasks.Source: D. Additional Real-to-Sim-to-Real… · p. 16

E. Noisy Observations and Actions

The section introduces observation and action noise wrappers to model stochasticity in perception and dynamics, then evaluates bilevel planning across varying noise levels.

E. Noisy Observations and Actions

  • Observation and action wrappers add noise to KinDER inputs and actions to simulate stochasticity in perception and dynamics; bilevel planning is shown at varying noise levels in Table VIII.The supplied passages do not report the results for those noise levels.Source: E. Noisy Observations and Actions · p. 16

Related papers

Loading 2604.25788v2…