arXiv 2603.071652026-03-07cs.RO

RoTri-Diff: 一种基于空间机器人-物体三元交互引导的扩散模型用于双臂操作

RoTri-Diff: A Spatial Robot-Object Triadic Interaction-Guided Diffusion Model for Bimanual Manipulation

School of Computer Science, Nanjing University（南京大学计算机科学学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）

Zixuan Chen, Nga Teng Chan, Yiwen Hou, Chenrui Tie, Zixuan Liu, Haonan Chen, Junting Chen, Jieqi Shi, Yang Gao, Jing Huo, Lin Shao

备注：: ICRA 2026

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AI总结：

RoTri-Diff通过建模机器人-物体三元交互关系，提出了一种基于扩散的模仿学习框架，以提高双臂操作的稳定性和协调性。

摘要：

双臂操作是机器人领域的一项基本技能，需要持续且精确地协调双臂。尽管模仿学习（IL）是获取这一能力的主要范式，但现有方法无论是以机器人为中心还是以物体为中心，往往忽视了双臂和被操作物体之间的动态几何关系。这种限制经常导致双臂碰撞、不稳定的抓取和在复杂任务中性能下降。为了解决这个问题，本文在双臂系统中显式地建模了机器人-物体三元交互（RoTri）表示，通过编码双臂和物体之间的相对6D姿态来捕捉它们的空间三元关系，并建立连续的三角几何约束。在此基础上，我们进一步引入RoTri-Diff，一种基于扩散的模仿学习框架，将RoTri约束与机器人关键姿态和物体运动结合在一个分层扩散过程中。这使能够生成稳定、协调的轨迹并在不同的双臂操作模式中实现稳健的执行。大量实验表明，我们的方法在11个代表性的RLBench2任务上比最先进的基线方法高出10.2%，并在4个具有挑战性的现实双臂任务中实现了稳定的性能。项目网站：https://rotri-diff.github.io/。

英文摘要：

Bimanual manipulation is a fundamental robotic skill that requires continuous and precise coordination between two arms. While imitation learning (IL) is the dominant paradigm for acquiring this capability, existing approaches, whether robot-centric or object-centric, often overlook the dynamic geometric relationship among the two arms and the manipulated object. This limitation frequently leads to inter-arm collisions, unstable grasps, and degraded performance in complex tasks. To address this, in this paper we explicitly models the Robot-Object Triadic Interaction (RoTri) representation in bimanual systems, by encoding the relative 6D poses between the two arms and the object to capture their spatial triadic relationship and establish continuous triangular geometric constraints. Building on this, we further introduce RoTri-Diff, a diffusion-based imitation learning framework that combines RoTri constraints with robot keyposes and object motion in a hierarchical diffusion process. This enables the generation of stable, coordinated trajectories and robust execution across different modes of bimanual manipulation. Extensive experiments show that our approach outperforms state-of-the-art baselines by 10.2% on 11 representative RLBench2 tasks and achieves stable performance on 4 challenging real-world bimanual tasks. Project website: https://rotri-diff.github.io/.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。