Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions

• URL: http://arxiv.org/abs/2604.11447v1
• Date: Mon, 13 Apr 2026 13:28:15 GMT
• Title: Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions
• Abstract summary: This paper presents a vision-based framework that enables a humanoid robot to imitate human movements while avoiding collisions.<n>Safety is enforced through commands to prevent both self-collisions and human-robot collisions.<n> Simulation results validate the effectiveness of the proposed framework for real-time collision-aware motion imitation.
• Score: 3.6775347543802686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring operational safety is critical for human-to-humanoid motion imitation. This paper presents a vision-based framework that enables a humanoid robot to imitate human movements while avoiding collisions. Human skeletal keypoints are captured by a single camera and converted into joint angles for motion retargeting. Safety is enforced through a Control Barrier Function (CBF) layer formulated as a Quadratic Program (QP), which filters imitation commands to prevent both self-collisions and human-robot collisions. Simulation results validate the effectiveness of the proposed framework for real-time collision-aware motion imitation.

Related papers

• Collision-Aware Humanoid Whole-Body Control under Imperfect Tracking Targets [11.010074477825272]
  Whole-body controllers (WBCs) execute commands that track targets while maintaining balance and stability.<n>Most WBCs are blind to scene geometry, which can lead to collisions due to perception, planning, or teleoperation errors.<n>We propose RECAL, a layer that wraps a blind WBC to trade off target tracking against collision avoidance using external scene geometry.
  arXiv  Detail & Related papers  (2026-09-14T22:22:02Z)
• Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots [9.096721498763717]
  Retargeting human motion to humanoid robots is critical for teleoperation, imitation learning and human-robot interaction.<n>This paper presents Human2Human, an unsupervised motion framework that transfers human motions to humanoid robot behaviors with high fidelity.<n> Experimental results show that the proposed method successfully retargets human motion to the Unitree G1 humanoid robot without paired data.
  arXiv  Detail & Related papers  (2026-06-02T10:53:06Z)
• Compliant Non-Prehensile Pushing Manipulation [2.526906424066039]
  We address the challenge of performing non-prehensile pushing operations with a compliant robotic manipulation system.<n>We extend a state-of-the-art pushing model to integrate it with impedance-controlled robots.<n>We develop a model predictive control framework built upon this model that enables compliant pushing through optimal modulation of the robot's position/velocity set-point.
  arXiv  Detail & Related papers  (2026-05-25T10:24:05Z)
• CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation [70.5382178207975]
  hIsight Perturbation (CHIP) is a plug-and-play module that enables controllable end-effector stiffness.<n>CHIP is easy to implement and requires neither data augmentation nor additional reward tuning.<n>We show that a generalist motion-tracking controller trained with CHIP can perform a diverse set of forceful manipulation tasks.
  arXiv  Detail & Related papers  (2025-12-16T18:56:04Z)
• From Language to Locomotion: Retargeting-free Humanoid Control via Motion Latent Guidance [55.31807046722006]
  Existing language-guided humanoid pipelines are cumbersome and untrustworthy.<n>We present RoboGhost, a language-free framework that conditions humanoid policies on language-grounded motion latents.<n>We show that RoboGhost substantially reduces deployment latency, improves success rates and tracking precision, and produces smooth, semantically aligned humanoids.
  arXiv  Detail & Related papers  (2025-10-16T17:57:47Z)
• 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)
• 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)
• KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills [58.73043119128804]
  This paper presents a physics-based humanoid control framework, aiming to master highly-dynamic human behaviors such as Kungfu and dancing.<n>For motion processing, we design a pipeline to extract, filter out, correct, and retarget motions, while ensuring compliance with physical constraints.<n>For motion imitation, we formulate a bi-level optimization problem to dynamically adjust the tracking accuracy tolerance.<n>In experiments, we train whole-body control policies to imitate a set of highly-dynamic motions.
  arXiv  Detail & Related papers  (2025-06-15T13:58:53Z)
• FABG : End-to-end Imitation Learning for Embodied Affective Human-Robot Interaction [3.8177867835232004]
  This paper proposes FABG (Facial Affective Behavior Generation), an end-to-end imitation learning system for human-robot interaction.<n>We develop an immersive virtual reality (VR) demonstration system that allows operators to perceive stereoscopic environments.<n>We deploy FABG on a real-world 25-degree-of-freedom humanoid robot, validating its effectiveness through four fundamental interaction tasks.
  arXiv  Detail & Related papers  (2025-03-03T09:58:04Z)
• COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion Estimation [98.05046790227561]
  COIN is a control-inpainting motion diffusion prior that enables fine-grained control to disentangle human and camera motions. COIN outperforms the state-of-the-art methods in terms of global human motion estimation and camera motion estimation.
  arXiv  Detail & Related papers  (2024-08-29T10:36:29Z)
• Hierarchical Procedural Framework for Low-latency Robot-Assisted Hand-Object Interaction [45.256762954338704]
  We propose a hierarchical procedural framework to enable robot-assisted hand-object interaction (HOI)<n>An open-loop hierarchy leverages the RGB-based 3D reconstruction of the human hand, based on which motion primitives have been designed to translate hand motions into robotic actions.<n>A case study of ring-wearing tasks indicates the potential application of this work in assistive technologies such as healthcare and manufacturing.
  arXiv  Detail & Related papers  (2024-05-29T21:20:16Z)
• I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning [8.97654258232601]
  We develop a framework to control humanoid robots through bounded residual reinforcement learning (I-CTRL)<n>I-CTRL excels in motion imitation with simple and unique rewards that generalize across five robots.<n>Our framework introduces an automatic priority scheduler to manage large-scale motion datasets.
  arXiv  Detail & Related papers  (2024-05-14T16:12:27Z)
• Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers [19.052211315080044]
  Bimanual handovers are crucial for transferring large, deformable or delicate objects. This paper proposes a framework for generating kinematically constrained human-like bimanual robot motions.
  arXiv  Detail & Related papers  (2024-02-22T13:19:02Z)
• ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space [9.806227900768926]
  This paper introduces a novel deep-learning approach for human-to-robot motion. Our method does not require paired human-to-robot data, which facilitates its translation to new robots. Our model outperforms existing works regarding human-to-robot similarity in terms of efficiency and precision.
  arXiv  Detail & Related papers  (2023-09-11T08:55:04Z)

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.