Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation

• URL: http://arxiv.org/abs/2604.20336v1
• Date: Wed, 22 Apr 2026 08:31:40 GMT
• Title: Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation
• Abstract summary: Co-manipulation requires multiple humans to synchronize their motions with a shared object.<n>Most existing motion generation approaches fail to account for payload-induced dynamics.<n>We propose a flow-matching framework that ensures the generated co-manipulation motions align with the intended goals.
• Score: 16.622175523077917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Co-manipulation requires multiple humans to synchronize their motions with a shared object while ensuring reasonable interactions, maintaining natural poses, and preserving stable states. However, most existing motion generation approaches are designed for single-character scenarios or fail to account for payload-induced dynamics. In this work, we propose a flow-matching framework that ensures the generated co-manipulation motions align with the intended goals while maintaining naturalness and effectiveness. Specifically, we first introduce a generative model that derives explicit manipulation strategies from the object's affordance and spatial configuration, which guide the motion flow toward successful manipulation. To improve motion quality, we then design an adversarial interaction prior that promotes natural individual poses and realistic inter-person interactions during co-manipulation. In addition, we also incorporate a stability-driven simulation into the flow matching process, which refines unstable interaction states through sampling-based optimization and directly adjusts the vector field regression to promote more effective manipulation. The experimental results demonstrate that our method achieves higher contact accuracy, lower penetration, and better distributional fidelity compared to state-of-the-art human-object interaction baselines. The code is available at https://github.com/boycehbz/StaCOM.

Related papers

• CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements [63.40365657068892]
  Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning.<n>Motivated by this gap, we adopt an implicit, data-driven approach guided by fine-grained hand-pose motions.<n>In both simulation and real-robot experiments, our framework enables stable and precise hand-to-gripper transfer of complex spatial manipulations.
  arXiv  Detail & Related papers  (2026-09-07T13:47:22Z)
• RoboForge: Physically Optimized Text-guided Whole-Body Locomotion for Humanoids [20.796118584632904]
  We present a unified latent-driven framework that bridges natural language and whole-body locomotion.<n>Our framework provides a practical path toward deployable text-guided humanoid intelligence.
  arXiv  Detail & Related papers  (2026-03-18T17:02:56Z)
• Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models [80.28579390566298]
  We introduce Interact2Ar, a text-conditioned autoregressive diffusion model for generating full-body, human-human interactions.<n>Hand kinematics are incorporated through dedicated parallel branches, enabling high-fidelity full-body generation.<n>Our model enables a series of downstream applications, including temporal motion composition, real-time adaptation to disturbances, and extension beyond dyadic to multi-person scenarios.
  arXiv  Detail & Related papers  (2025-12-22T18:59:50Z)
• Decoupled Generative Modeling for Human-Object Interaction Synthesis [35.78156236836254]
  Existing approaches often require manually specified intermediate waypoints and place all optimization objectives on a single network.<n>We propose Decoupled Generative Modeling for Human-Object Interaction Synthesis (DecHOI)<n>A trajectory generator first produces human and object trajectories without prescribed waypoints, and an action generator conditions on these paths to synthesize detailed motions.
  arXiv  Detail & Related papers  (2025-12-22T05:33:59Z)
• One-shot Humanoid Whole-body Motion Learning [18.375746497945023]
  Methods typically require multiple training samples per motion category.<n>We propose a novel approach that trains effective humanoid motion policies using only a single non-walking target motion sample.
  arXiv  Detail & Related papers  (2025-10-29T07:48:10Z)
• MoReact: Generating Reactive Motion from Textual Descriptions [57.642436102978245]
  MoReact is a diffusion-based method designed to disentangle the generation of global trajectories and local motions sequentially.<n>Our experiments, utilizing data adapted from a two-person motion dataset, demonstrate the efficacy of our approach.
  arXiv  Detail & Related papers  (2025-09-28T14:31:41Z)
• CoopDiff: Anticipating 3D Human-object Interactions via Contact-consistent Decoupled Diffusion [62.93198247045824]
  3D human-object interaction (HOI) anticipation aims to predict the future motion of humans and their manipulated objects, conditioned on the historical context.<n>We propose a novel contact-consistent decoupled diffusion framework CoopDiff, which employs two distinct branches to decouple human and object motion modeling.
  arXiv  Detail & Related papers  (2025-08-10T03:29:17Z)
• CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects [14.230098033626744]
  Whole-body manipulation of articulated objects is a critical yet challenging task with broad applications in virtual humans and robotics.<n>We propose a novel coordinated diffusion noise optimization framework to achieve realistic whole-body motion.<n>We conduct extensive experiments demonstrating that our method outperforms existing approaches in motion quality and physical plausibility.
  arXiv  Detail & Related papers  (2025-05-27T17:11:50Z)
• DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation [78.60543357822957]
  Dexterous manipulation with contact-rich interactions is crucial for advanced robotics.<n>We introduce DexHandDiff, an interaction-aware diffusion planning framework for adaptive dexterous manipulation.<n>Our framework achieves an average of 70.7% success rate on goal adaptive dexterous tasks, highlighting its robustness and flexibility in contact-rich manipulation.
  arXiv  Detail & Related papers  (2024-11-27T18:03:26Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Contact-Aware Retargeting of Skinned Motion [49.71236739408685]
  This paper introduces a motion estimation method that preserves self-contacts and prevents interpenetration. The method identifies self-contacts and ground contacts in the input motion, and optimize the motion to apply to the output skeleton. In experiments, our results quantitatively outperform previous methods and we conduct a user study where our retargeted motions are rated as higher-quality than those produced by recent works.
  arXiv  Detail & Related papers  (2021-09-15T17:05:02Z)

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.