RoTri-Diff: A Spatial Robot-Object Triadic Interaction-Guided Diffusion Model for Bimanual Manipulation
Zixuan Chen, Nga Teng Chan, Yiwen Hou, Chenrui Tie, Zixuan Liu, Haonan Chen, Junting Chen, Jieqi Shi, Yang Gao, Jing Huo, Lin Shao

TL;DR
RoTri-Diff introduces a novel diffusion-based imitation learning framework that explicitly models the spatial triadic relationship among robot arms and objects, improving coordination and stability in bimanual manipulation tasks.
Contribution
It proposes the RoTri representation for modeling spatial relationships and integrates it into a diffusion framework for enhanced bimanual manipulation.
Findings
Outperforms state-of-the-art by 10.2% on RLBench2 tasks
Achieves stable performance on 4 real-world bimanual tasks
Effectively captures spatial relationships to reduce collisions and improve stability
Abstract
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Motor Control and Adaptation · Reinforcement Learning in Robotics
