ThorArena: Benchmarking Humanoid Physical Interaction with Human Motion-Force Demonstrations

• URL: http://arxiv.org/abs/2607.06052v1
• Date: Tue, 07 Jul 2026 09:26:51 GMT
• Title: ThorArena: Benchmarking Humanoid Physical Interaction with Human Motion-Force Demonstrations
• Abstract summary: ThorArena is a benchmark for evaluating force-aware humanoid interaction based on human demonstrations with synchronized motion and force measurements.<n>We propose force-aware evaluation metrics that jointly assess whole-body tracking accuracy, robustness under different force levels, control effort, and episode survival.
• Score: 24.273078797508884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humanoid robots are increasingly expected to perform contact-rich tasks that require not only accurate whole-body motion but also robust physical interaction with surrounding objects and humans. Although recent advances in humanoid motion imitation and whole-body control have achieved remarkable tracking performance, existing datasets and benchmarks primarily focus on kinematic motion while largely overlooking synchronized interaction forces. As a result, current evaluations fail to capture how external interaction forces affect tracking accuracy, stability, and control robustness. In this paper, we present ThorArena, a benchmark for evaluating force-aware humanoid interaction based on human demonstrations with synchronized motion and force measurements. We collect a real-world interaction dataset that simultaneously captures whole-body human motion and forces exerted by both hands across six representative physical interaction tasks. Based on these demonstrations, we propose force-aware evaluation metrics that jointly assess whole-body tracking accuracy, robustness under different force levels, control effort, and episode survival through the Force-Aware Tracking Score (FATS) and complementary diagnostic metrics. We further establish a unified benchmark protocol that replays recorded interaction forces in simulation and provides a standardized evaluation interface for different humanoid control policies. Experiments on representative whole-body control policies demonstrate that force-aware evaluation reveals substantial performance differences that remain largely hidden under conventional no-force evaluation. ThorArena provides a practical and reproducible framework for studying force-aware humanoid interaction and offers a new benchmark for evaluating contact-rich humanoid behaviors.

Related papers

• ADAPT: Analytical Disturbance-Aware Policy Training for Humanoid Locomotion [21.269965799228576]
  Analytical Disturbance-Aware Policy Training (ADAPT) is a framework that equips humanoid policies with a physically grounded disturbance observer.<n>ADAPT estimates residual force/torque online with the accessible robot dynamics, without requiring force/torque sensors.
  arXiv  Detail & Related papers  (2026-06-15T10:47:18Z)
• SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids [13.456157050156643]
  SixthSense infers whole-body contact timing, location, and wrenches from proprioception and IMU data alone.<n>It serves as a plug-and-play perception module for applications including collision detection, physical human-robot interaction, and force-feedback teleoperation.
  arXiv  Detail & Related papers  (2026-05-02T12:57:27Z)
• Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot [56.377161933570335]
  Humans naturally exploit a "weightless" state during non-self-stabilizing (NSS) motions.<n>Inspired by this biological mechanism, we propose a weightlessness-state auto-labeling strategy for dataset annotation.<n>Our work bridges the gap between precise trajectory tracking and adaptive environmental interaction, offering a biologically-inspired solution for contact-rich humanoid control.
  arXiv  Detail & Related papers  (2026-04-23T07:10:05Z)
• Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots [54.54929302887405]
  We present the Human-Humanoid Motion dataset, which consists of 1,000 motion sequences spanning 15 action categories.<n>All motion sequences are converted into SMPL-X representations to eliminate the influence of visual appearance.<n>Analysis of the collected data reveals that humanoid motions still exhibit noticeable deviations from human movements.
  arXiv  Detail & Related papers  (2026-03-06T11:40:56Z)
• GentleHumanoid: Learning Upper-body Compliance for Contact-rich Human and Object Interaction [14.278503723930998]
  GentleHumanoid is a framework that integrates impedance control into a whole-body motion tracking policy to achieve upper-body compliance.<n>We evaluate our approach in both simulation and on the Unitree G1 humanoid across tasks requiring different levels of compliance.
  arXiv  Detail & Related papers  (2025-11-06T18:59:33Z)
• ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning [59.64325421657381]
  Humanoid whole-body loco-manipulation promises transformative capabilities for daily service and warehouse tasks.<n>We introduce ResMimic, a two-stage residual learning framework for precise and expressive humanoid control from human motion data.<n>Results show substantial gains in task success, training efficiency, and robustness over strong baselines.
  arXiv  Detail & Related papers  (2025-10-06T17:47:02Z)
• PP-Motion: Physical-Perceptual Fidelity Evaluation for Human Motion Generation [84.40992836326613]
  Human motion generation has found widespread applications in AR/VR, film, sports, and medical rehabilitation.<n>Previous approaches have attempted at motion fidelity evaluation using human perception or physical constraints.<n>We propose PP-Motion, a novel data-driven metric to evaluate both physical and perceptual fidelity of human motion.
  arXiv  Detail & Related papers  (2025-08-11T16:59:15Z)
• Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis [51.95817740348585]
  Human-X is a novel framework designed to enable immersive and physically plausible human interactions across diverse entities.<n>Our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner.<n>Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction.
  arXiv  Detail & Related papers  (2025-08-04T06:35:48Z)
• ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection [0.0]
  ForcePose is a novel deep learning framework that estimates applied forces by combining human pose estimation with object detection.<n>We've developed a specialized neural network that processes both spatial and temporal features to predict force magnitude and direction without needing any physical sensors.
  arXiv  Detail & Related papers  (2025-03-28T12:13:56Z)
• ExBody2: Advanced Expressive Humanoid Whole-Body Control [16.69009772546575]
  We propose a method for producing whole-body tracking controllers that are trained on both human motion capture and simulated data.<n>We use a teacher policy to produce intermediate data that better conforms to the robot's kinematics.<n>We observed significant improvement of tracking performance after fine-tuning on a small amount of data.
  arXiv  Detail & Related papers  (2024-12-17T18:59:51Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)

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.