PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning
This paper introduces PIMbot, a framework that manipulates outcomes in multi-robot RL via two complementary levers: incentive manipulation of the reward channel and policy manipulation of an agent's own actions. An adaptive multi-objective controller balances these levers online. Experiments in Gazebo simulation and on NVIDIA Jetson Orin Nano embedded device demonstrate effectiveness, positioning PIMbot as a stress-test tool for vulnerabilities in multi-robot cooperation.
Article intelligence
Key points
- PIMbot uses two manipulation levers: reward channel incentive manipulation and policy manipulation.
- An adaptive multi-objective controller balances the levers online.
- Effectiveness validated in Gazebo simulation and on real NVIDIA Jetson Orin Nano hardware.
- PIMbot serves as a stress-test tool to expose vulnerabilities in multi-robot cooperative tasks.
Why it matters
This matters because pIMbot uses two manipulation levers: reward channel incentive manipulation and policy manipulation.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.23027] PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning
[Submitted on 21 May 2026]
Title:PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning
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Abstract:Recent research has demonstrated the potential of reinforcement learning in effective multi-robot collaboration, particularly in social dilemmas where robots face a trade-off between self-interest and collective benefits. However, environmental factors such as miscommunication and adversarial robots can impact cooperation, making it crucial to explore how multi-robot communication can be manipulated to achieve different outcomes. This paper presents PIMbot, a framework that manipulates outcomes via two complementary levers: (i) incentive manipulation of the reward channel and (ii) policy manipulation of an agent's own actions. An adaptive multi-objective controller balances these levers in an online manner. Our work introduces a novel approach to manipulation in recent multi-agent RL social dilemmas that utilize a unique reward function for incentivization. By utilizing our proposed PIMbot mechanisms, a robot is able to manipulate the social dilemma environment effectively. Comprehensive experimental results demonstrate the effectiveness of our proposed methods in the Gazebo-simulated multi-robot environment. Moreover, a real embedded device case study on NVIDIA Jetson Orin Nano quantifies system cost and validates PIMbot's effectiveness on realistic autonomous embedded systems scenarios beyond simulation. Together, these results position PIMbot as a rigorous stress-test tool exposing critical vulnerabilities in multi-robot cooperative tasks.
Comments: Extension version of IROS'23
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.23027 [cs.RO]
(or arXiv:2605.23027v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.23027
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zexin Li [view email] [v1] Thu, 21 May 2026 20:50:47 UTC (6,691 KB)
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