紧张过度 发表于 2025-3-25 03:21:40
Conference proceedings 2024 28–30, 2022, in Montbéliard, France..The contributions are covering a broad scope of topics within distributed robotics including mobile sensor networks, unmanned aerial vehicles, multi-agent systems, algorithms for multi-robot systems, modular robots, swarm robotics, and reinforcement learning orfloodgate 发表于 2025-3-25 10:56:15
,The Benefits of Interaction Constraints in Distributed Autonomous Systems,rm robotics alike have highlighted the impact that the interactions between agents have on the collective behaviours exhibited by the system. In this paper, we seek to highlight the role that the underlying interaction network plays in determining the performance of the collective behaviour of a sysinsincerity 发表于 2025-3-25 13:23:15
http://reply.papertrans.cn/29/2818/281746/281746_23.pngBlazon 发表于 2025-3-25 19:48:23
,VMAS: A Vectorized Multi-agent Simulator for Collective Robot Learning,lti-Agent Reinforcement Learning (MARL) is gaining increasing attention in the robotics community as a promising solution to tackle such problems. Nevertheless, we still lack the tools that allow us to . and . find solutions to large-scale collective learning tasks. In this work, we introduce the VeLAST 发表于 2025-3-25 20:36:38
http://reply.papertrans.cn/29/2818/281746/281746_25.pngBone-Scan 发表于 2025-3-26 04:09:54
http://reply.papertrans.cn/29/2818/281746/281746_26.pngContort 发表于 2025-3-26 05:58:16
http://reply.papertrans.cn/29/2818/281746/281746_27.pngannexation 发表于 2025-3-26 11:36:08
,A Decentralized Cooperative Approach to Gentle Human Transportation with Mobile Robots Based on Tacile objects including humans which may move during the transport, with as little burden as possible. We propose the adoption of a flexible tri-axis tactile sensor with thickness at the top of the robot on which the object is mounted for safe support and state monitoring. We improved the leader-folloALLEY 发表于 2025-3-26 13:08:07
,Sparse Sensing in Ergodic Optimization,sage of limited resources while effectively exploring an area is vital in scenarios where sensing is expensive, has adverse effects, or is exhaustive. We approach this problem using ergodic search techniques, which optimize how long a robot spends in a region based on the likelihood of obtaining inf反馈 发表于 2025-3-26 17:53:36
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