MorphoGuard: A Morphology-Based Whole-Body Interactive Motion Controller

• URL: http://arxiv.org/abs/2604.01517v1
• Date: Thu, 02 Apr 2026 01:13:29 GMT
• Title: MorphoGuard: A Morphology-Based Whole-Body Interactive Motion Controller
• Abstract summary: We propose a new control approach that explicitly manages arbitrary contact combinations, aiming to endow robots with whole-body interactive capabilities.<n>We develop a morphology-constrained WBC network (MorphoGuard)-which is trained on a self-constructed dual-arm physical and simulation platform.<n> Experimental results show that the proposed method achieves a contact point management error of approximately 1 cm, demonstrating its effectiveness in whole-body interactive control.
• Score: 8.867753335261563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whole-body control (WBC) has demonstrated significant advantages in complex interactive movements of high-dimensional robotic systems. However, when a robot is required to handle dynamic multi-contact combinations along a single kinematic chain-such as pushing open a door with its elbow while grasping an object-it faces major obstacles in terms of complex contact representation and joint configuration coupling. To address this, we propose a new control approach that explicitly manages arbitrary contact combinations, aiming to endow robots with whole-body interactive capabilities. We develop a morphology-constrained WBC network (MorphoGuard)-which is trained on a self-constructed dual-arm physical and simulation platform. A series of model recommendation experiments are designed to systematically investigate the impact of backbone architecture, fusion strategy, and model scale on network performance. To evaluate the control performance, we adopt a multi-object interaction task as the benchmark, requiring the model to simultaneously manipulate multiple target objects to specified positions. Experimental results show that the proposed method achieves a contact point management error of approximately 1 cm, demonstrating its effectiveness in whole-body interactive control.

Related papers

• AnyWorld: Factorized Egocentric World Models for Cross-Embodiment Generalization [88.25615187835321]
  We propose AnyWorld, a cross-embodiment world modeling framework.<n>Our model factorizes an interaction into action, camera, and embodiment.<n>We train the model with large-scale human interaction pretraining.
  arXiv  Detail & Related papers  (2026-08-29T12:55:15Z)
• Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning [128.76135832773102]
  Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world.<n>Recent robot learning methods have made substantial progress by integrating force, tactile, vision, language, and proprioceptive sensing into learned manipulation policies.<n>Despite these advances, existing surveys have not explicitly reviewed force- and tactile-aware robot learning from a unified perspective.
  arXiv  Detail & Related papers  (2026-08-02T14:22:01Z)
• X-OP: Cross-Morphology Whole-Body Teleoperation via MPC Retargeting [3.134050607080983]
  Whole-body teleoperation is essential for scalable robot data collection in loco-manipulation tasks.<n>We propose a hierarchical whole-body teleoperation framework driven by a single XR device.<n>We show higher success rates on whole-body control tasks for both a humanoid and a mobile manipulator.
  arXiv  Detail & Related papers  (2026-06-06T01:50:59Z)
• M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking [45.636871702422134]
  Multi-Modal Mimic is a versatile whole-body control framework for humanoid robots.<n>It unifies heterogeneous motion reference modalities, including robot joint angles, human pose trajectories, and end-effector poses.<n>It achieves sim-to-real transfer across multiple motion reference modalities without modality-specific retraining.
  arXiv  Detail & Related papers  (2026-06-03T12:52:37Z)
• Structured interactions improve distributed coordination beyond model scaling in a real-world multi-robot system [48.701675182639]
  We investigate a system-level design question in real-world multi-robot coordination.<n>Is restructuring communication among robots yield larger gains than increasing onboard model size?<n>Switching from fully connected to modular hierarchical interactions improves normalised performance by 47 points (0--100)
  arXiv  Detail & Related papers  (2026-05-28T03:21:52Z)
• Efficient and Compliant Control Framework for Versatile Human-Humanoid Collaborative Transportation [2.4087148947930634]
  We present a control framework that enables humanoid robots to perform collaborative transportation tasks with a human partner.<n>The framework supports both translational and rotational motions, which are fundamental to co-transport scenarios.<n>It comprises three components: a high-level planner, a low-level controller, and a stiffness modulation mechanism.
  arXiv  Detail & Related papers  (2025-12-08T18:52:10Z)
• Learning Video Generation for Robotic Manipulation with Collaborative Trajectory Control [72.00655365269]
  We present RoboMaster, a novel framework that models inter-object dynamics through a collaborative trajectory formulation.<n>Unlike prior methods that decompose objects, our core is to decompose the interaction process into three sub-stages: pre-interaction, interaction, and post-interaction.<n>Our method outperforms existing approaches, establishing new state-of-the-art performance in trajectory-controlled video generation for robotic manipulation.
  arXiv  Detail & Related papers  (2025-06-02T17:57:06Z)
• 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)
• V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated Objects [51.79035249464852]
  We present a framework for learning multi-arm manipulation of articulated objects. Our framework includes a variational generative model that learns contact point distribution over object rigid parts for each robot arm.
  arXiv  Detail & Related papers  (2021-11-07T02:31:09Z)
• Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation [16.79185733369416]
  We propose a two-stage architecture for autonomous interaction with large articulated objects in unknown environments. The first stage uses a learned model to estimate the articulated model of a target object from an RGB-D input and predicts an action-conditional sequence of states for interaction. The second stage comprises of a whole-body motion controller to manipulate the object along the generated kinematic plan.
  arXiv  Detail & Related papers  (2021-03-18T21:32:18Z)
• Learning Physical Constraints with Neural Projections [16.09436906471513]
  We propose a new family of neural networks to predict the behaviors of physical systems by learning their underpinning constraints. A neural projection operator lies at the heart of our approach, composed of a lightweight network with an embedded recursion architecture. We demonstrated the efficacy of our approach by learning a set of challenging physical systems all in a unified and simple fashion.
  arXiv  Detail & Related papers  (2020-06-23T04:19:04Z)

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.