MAAP: Multi-Agent Active Perception for Collaborative Manipulation

原文：MAAP: Multi-Agent Active Perception for Collaborative Manipulation

作者：Bruno N. Y. Chen, Li Kang, Heng Zhou, Xiufeng Song, Zhemeng Zhang, Jiahua Ma, Yiran Qin

来源：arXiv cs.RO（机器人）

正文

Computer Science > Robotics

arXiv:2609.21929v1 (cs)

[Submitted on 18 Sep 2026]

Title:MAAP: Multi-Agent Active Perception for Collaborative Manipulation

Authors:Bruno N.Y. Chen, Li Kang, Heng Zhou, Xiufeng Song, Zhemeng Zhang, Jiahua Ma, Yiran Qin

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Abstract:Multi-agent manipulation naturally produces multiple task-driven viewpoints: every arm carries a wrist camera and moves through the scene while acting. Yet these observations are typically underutilized, and active perception in manipulation is still often treated as requiring a dedicated sensing agent. We introduce MAAP (Multi-Agent Active Perception), in which every arm is dual-purpose: it executes manipulation actions and, through the wrist camera it carries, simultaneously serves as a moving viewpoint for the team. We pair this with RAIL (Role-Aware Imitation Learning), a controller that predicts each arm’s current role alongside its action chunk and conditions action generation on it, representing role-dependent actions within one network. Across four simulated tasks, widening the perception regime lifts average success from 56.5% with a fixed camera to 62.5% with one active wrist view and 70.0% with all of them, while MAAP+RAIL reaches 79.2%. RAIL’s additional gain is concentrated on the three-arm Microwave task, where success rises from 47% to 82% on identical multi-wrist inputs. On a dual-arm platform, MAAP+RAIL succeeds in 14 of 20 placement trials compared with 0 of 20 for fixed-view ACT. Collaborative manipulation can thus serve as an active perception mechanism in its own right.

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Robotics (cs.RO)

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arXiv:2609.21929 [cs.RO]

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arXiv:2609.21929v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.21929

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机器人

由「前沿雷达」于 2026-09-18 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。