让机器人用身体多点交互，精准控制接触点位置。
MorphoGuard: A Morphology-Based Whole-Body Interactive Motion Controller
- 基于形态约束设计新控制网络，显式管理任意接触组合。
- 实测接触点误差约1厘米，实现高精度全身交互控制。
- 适合需要复杂物理交互的双臂机器人研究与应用。
全身体控（WBC）在高维机器人系统的复杂交互运动中展现出显著优势。然而，当机器人需沿单一运动链处理动态多接触组合（如用肘部推门同时抓取物体）时，面临接触表示复杂和关节配置耦合等重大挑战。为此，我们提出一种新控制方法，显式管理任意接触组合，旨在赋予机器人全身体交互能力。我们构建了基于形态约束的全身体控网络（MorphoGuard），并在自建的双臂物理与仿真平台上进行训练。通过一系列模型推荐实验，系统研究了骨干架构、融合策略及模型规模对性能的影响。为评估控制效果，采用多目标交互任务作为基准，要求模型同时将多个目标物体移动至指定位置。实验结果表明，所提方法在接触点管理上误差约为1厘米，验证了其在全身体交互控制中的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。