Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots

• URL: http://arxiv.org/abs/2603.06181v1
• Date: Fri, 06 Mar 2026 11:40:56 GMT
• Title: Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots
• Abstract summary: 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.
• Score: 54.54929302887405
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Humanoid robots have achieved significant progress in motion generation and control, exhibiting movements that appear increasingly natural and human-like. Inspired by the Turing Test, we propose the Motion Turing Test, a framework that evaluates whether human observers can discriminate between humanoid robot and human poses using only kinematic information. To facilitate this evaluation, we present the Human-Humanoid Motion (HHMotion) dataset, which consists of 1,000 motion sequences spanning 15 action categories, performed by 11 humanoid models and 10 human subjects. All motion sequences are converted into SMPL-X representations to eliminate the influence of visual appearance. We recruited 30 annotators to rate the human-likeness of each pose on a 0-5 scale, resulting in over 500 hours of annotation. Analysis of the collected data reveals that humanoid motions still exhibit noticeable deviations from human movements, particularly in dynamic actions such as jumping, boxing, and running. Building on HHMotion, we formulate a human-likeness evaluation task that aims to automatically predict human-likeness scores from motion data. Despite recent progress in multimodal large language models, we find that they remain inadequate for assessing motion human-likeness. To address this, we propose a simple baseline model and demonstrate that it outperforms several contemporary LLM-based methods. The dataset, code, and benchmark will be publicly released to support future research in the community.

Related papers

• X-Morph: Human Motion Priors for Scalable Robot Learning Across Morphologies [6.9484599086239465]
  We present X-Morph, a human-motion-to-robot-behavior that converts human motion into loco-manipulation policies.<n>A cross-morphology stage converts human motions into kinematically plausible, intent-preserving robot references.<n>The resulting policies track diverse retargeted motions, repurpose to unseen human motions, and support use cases including video-based teleoperation.
  arXiv  Detail & Related papers  (2026-06-29T13:31:27Z)
• FRoM-W1: Towards General Humanoid Whole-Body Control with Language Instructions [147.04372611893032]
  We present FRoM-W1, an open-source framework designed to achieve general humanoid whole-body motion control using natural language.<n>We extensively evaluate FRoM-W1 on Unitree H1 and G1 robots.<n>Results demonstrate superior performance on the HumanML3D-X benchmark for human whole-body motion generation.
  arXiv  Detail & Related papers  (2026-01-19T07:59:32Z)
• SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control [85.91101551600978]
  We show that scaling up model capacity, data, and compute yields a generalist humanoid controller capable of creating natural and robust whole-body movements.<n>We build a foundation model for motion tracking by scaling along three axes: network size, dataset volume, and compute.<n>We show the practical utility of our model through two mechanisms: (1) a real-time universal kinematic planner that bridges motion tracking to downstream task execution, enabling natural and interactive control, and (2) a unified token space that supports various motion input interfaces.
  arXiv  Detail & Related papers  (2025-11-11T04:37:40Z)
• 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)
• Massively Multi-Person 3D Human Motion Forecasting with Scene Context [13.197408989895102]
  We propose a scene-aware social transformer model (SAST) to forecast long-term (10s) human motion motion. We combine a temporal convolutional encoder-decoder architecture with a Transformer-based bottleneck that allows us to efficiently combine motion and scene information. Our model outperforms other approaches in terms of realism and diversity on different metrics and in a user study.
  arXiv  Detail & Related papers  (2024-09-18T17:58:51Z)
• Aligning Human Motion Generation with Human Perceptions [51.831338643012444]
  We propose a data-driven approach to bridge the gap by introducing a large-scale human perceptual evaluation dataset, MotionPercept, and a human motion critic model, MotionCritic.<n>Our critic model offers a more accurate metric for assessing motion quality and could be readily integrated into the motion generation pipeline.
  arXiv  Detail & Related papers  (2024-07-02T14:01:59Z)
• Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations [61.659439423703155]
  TOHO: Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations. Our method generates continuous motions that are parameterized only by the temporal coordinate. This work takes a step further toward general human-scene interaction simulation.
  arXiv  Detail & Related papers  (2023-03-23T09:31:56Z)
• Action2Motion: Conditioned Generation of 3D Human Motions [28.031644518303075]
  We aim to generateplausible human motion sequences in 3D. Each sampled sequence faithfully resembles anaturalhuman bodyarticulation dynamics. A new 3D human motion dataset, HumanAct12, is also constructed.
  arXiv  Detail & Related papers  (2020-07-30T05:29:59Z)

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.