最近的研究证明了强化学习在多机器人有效协作中的潜力，特别是在机器人面临自身利益与集体利益权衡的社会困境中。然而，沟通不畅和对抗性机器人等环境因素可能影响合作，因此探索如何操纵多机器人通信以实现不同结果至关重要。本文提出了PIMbot，一个通过两种互补杠杆操纵结果的框架：(i) 奖励通道的激励操纵和(ii) 智能体自身动作的策略操纵。一个自适应多目标控制器在线平衡这些杠杆。我们的工作引入了一种新颖的方法来操纵最近的多智能体强化学习社会困境，这些困境利用独特的奖励函数进行激励。通过利用我们提出的PIMbot机制，机器人能够有效地操纵社会困境环境。全面的实验结果证明了我们提出的方法在Gazebo模拟的多机器人环境中的有效性。此外，在NVIDIA Jetson Orin Nano上的真实嵌入式设备案例研究量化了系统成本，并验证了PIMbot在超越仿真的现实自主嵌入式系统场景中的有效性。这些结果共同将PIMbot定位为一个严格的压力测试工具，暴露了多机器人协作任务中的关键漏洞。
英文摘要：
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.
College of Engineering and Physical Sciences, Aston University（工程与物理科学学院，阿斯顿大学）；Department of Computer Science Department, Loughborough University（计算机科学系，洛桑大学）
Manipulate as Human: Learning Task-oriented Manipulation Skills by Adversarial Motion Priors
ParisTech Elite Institute of Technology, Shanghai Jiao Tong University（上海交通大学巴黎高科精英技术研究院）；Department of Automation, Shanghai Jiao Tong University（上海交通大学自动化学院）；MoE Key Lab of Artificial Intelligence and AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能与AI研究院）
