Learning Human-Humanoid Coordination for Collaborative Object Carrying

• URL: http://arxiv.org/abs/2510.14293v1
• Date: Thu, 16 Oct 2025 04:36:25 GMT
• Title: Learning Human-Humanoid Coordination for Collaborative Object Carrying
• Authors: Yushi Du, Yixuan Li, Baoxiong Jia, Yutang Lin, Pei Zhou, Wei Liang, Yanchao Yang, Siyuan Huang,
• Abstract summary: We propose a proprioception-only reinforcement learning approach, COLA, that combines leader and follower behaviors within a single policy.<n>We evaluate our approach through comprehensive simulator and real-world experiments on collaborative carrying tasks.<n>Our method enables compliant human-humanoid collaborative carrying without requiring external sensors or complex interaction models.
• Score: 37.81901077587862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-humanoid collaboration shows significant promise for applications in healthcare, domestic assistance, and manufacturing. While compliant robot-human collaboration has been extensively developed for robotic arms, enabling compliant human-humanoid collaboration remains largely unexplored due to humanoids' complex whole-body dynamics. In this paper, we propose a proprioception-only reinforcement learning approach, COLA, that combines leader and follower behaviors within a single policy. The model is trained in a closed-loop environment with dynamic object interactions to predict object motion patterns and human intentions implicitly, enabling compliant collaboration to maintain load balance through coordinated trajectory planning. We evaluate our approach through comprehensive simulator and real-world experiments on collaborative carrying tasks, demonstrating the effectiveness, generalization, and robustness of our model across various terrains and objects. Simulation experiments demonstrate that our model reduces human effort by 24.7%. compared to baseline approaches while maintaining object stability. Real-world experiments validate robust collaborative carrying across different object types (boxes, desks, stretchers, etc.) and movement patterns (straight-line, turning, slope climbing). Human user studies with 23 participants confirm an average improvement of 27.4% compared to baseline models. Our method enables compliant human-humanoid collaborative carrying without requiring external sensors or complex interaction models, offering a practical solution for real-world deployment.

Related papers

• A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration [41.43425233041408]
  This paper introduces a HAR system combining a modular data glove equipped with Inertial Measurement Units and a vision-based tactile sensor to capture hand activities in contact with a robot.<n>We tested our activity recognition approach under different conditions, including offline classification of segmented sequences, real-time classification under static conditions, and a realistic HRC scenario.<n>The experimental results show a high accuracy for all the tasks, suggesting that multiple collaborative settings could benefit from this multi-modal approach.
  arXiv  Detail & Related papers  (2026-02-02T10:14:19Z)
• 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)
• Moving Out: Physically-grounded Human-AI Collaboration [21.627325065213725]
  We introduce Moving Out, a new human-AI collaboration benchmark.<n>We evaluate models' abilities to adapt to diverse human behaviors and unseen physical attributes.<n>Our experiments show that BASS outperforms state-of-the-art models in AI-AI and human-AI collaboration.
  arXiv  Detail & Related papers  (2025-07-24T17:57:18Z)
• 3HANDS Dataset: Learning from Humans for Generating Naturalistic Handovers with Supernumerary Robotic Limbs [64.99122701615151]
  Supernumerary robotic limbs (SRLs) are robotic structures integrated closely with the user's body.<n>We present 3HANDS, a novel dataset of object handover interactions between a participant performing a daily activity and another participant enacting a hip-mounted SRL in a naturalistic manner.<n>We present three models that generate naturalistic handover trajectories, one that determines the appropriate handover endpoints, and a third that predicts the moment to initiate a handover.
  arXiv  Detail & Related papers  (2025-03-06T17:23:55Z)
• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [50.657070334404835]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments.<n>We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions.<n>Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics [44.30880626337739]
  CooHOI is a framework designed to tackle the challenge of multi-humanoid object transportation problem. A single humanoid character learns to interact with objects through imitation learning from human motion priors. Then, the humanoid learns to collaborate with others by considering the shared dynamics of the manipulated object.
  arXiv  Detail & Related papers  (2024-06-20T17:59:22Z)
• Large Language Model-based Human-Agent Collaboration for Complex Task Solving [94.3914058341565]
  We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving. We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
  arXiv  Detail & Related papers  (2024-02-20T11:03:36Z)
• Learning Multimodal Latent Dynamics for Human-Robot Interaction [18.68936554172693]
  This article presents a method for learning well-coordinated Human-Robot Interaction (HRI) from Human-Human Interactions (HHI)<n>We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational Autoencoder to model a joint distribution over the interacting agents.<n>We find that users perceive our method as more human-like, timely, and accurate and rank our method with a higher degree of preference over other baselines.
  arXiv  Detail & Related papers  (2023-11-27T23:56:59Z)
• InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint [67.6297384588837]
  We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. We demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model.
  arXiv  Detail & Related papers  (2023-11-27T14:32:33Z)
• Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots [119.55240471433302]
  Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in home environments. It addresses challenges in modeling complex deformable bodies and diversity in appearance and motion. Human-in-the-loop infrastructure enables real human interaction with simulated robots via mouse/keyboard or a VR interface.
  arXiv  Detail & Related papers  (2023-10-19T17:29:17Z)
• Increased Complexity of a Human-Robot Collaborative Task May Increase the Need for a Socially Competent Robot [0.0]
  This study investigates how task complexity affects human perception and acceptance of their robot partner. We propose a human-based robot control model for obstacle avoidance that can account for the leader-follower dynamics.
  arXiv  Detail & Related papers  (2022-07-11T11:43:27Z)

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.