The paper introduces COMPASS, a decentralized architecture that uses spatial transformers to generate local feedback tokens for large collectives of AI agents in robotics. By aggregating multi‑hop messages, COMPASS enables scalable control of up to 1024 robots, achieving cohesive flocking formations and accurate execution of natural language commands. Experiments show that structured diversity in input commands improves performance and that learned feedback tokens outperform hand‑crafted raw state feedback.
By Frederic Vatnsdal, Roshan Gopal, Romina Garcia Camargo, Vijay Kumar, Alejandro Ribeiro
arXiv:2607. 10991v1 Announce Type: cross Abstract: As mobile robots become more integrated into everyday human environments, social robot navigation is becoming essential for ensuring human comfort, safety, and trust.
By Ali Ahmadi, Hamed Rahimi, Adrien Jacquet Cretides, Marie Samson, Mahdi Khoramshahi, Mohamed Chetouani
DuoMind is a distributed hierarchical framework that enables multi-robot coordination by combining vision-language-action models for low‑level execution with vision-language models for high‑level reasoning and inter‑robot communication. Each robot’s orchestrator processes task instructions, local observations, and messages from peers to generate precise action commands and semantic messages for other robots. The authors introduce RoboPoly, a benchmark of long‑horizon manipulation tasks, and show through experiments that DuoMind improves multi‑robot task performance, with ablation studies highlighting the roles of hierarchical orchestration and semantic communication.
By Hanchu Zhou, Dechen Gao, Hang Wang, Brendan Lynch, Boqi Zhao, Qiyao Ma, Raman Goyal, Junshan Zhang
The paper introduces the Shared Recurrent Memory Transformer (SRMT), a decentralized multi‑agent reinforcement learning framework that uses a global memory workspace for agents to broadcast and query each other’s learned states. SRMT is evaluated on the Partially Observable Multi‑Agent Pathfinding (PO‑MAPF) problem, showing that shared memory enables emergent coordination even with minimal reward guidance and outperforms existing baselines on the Bottleneck task and scales competitively on larger POGEMA maps. The authors provide open‑source code for training and evaluation on GitHub.
By Alsu Sagirova, Yuri Kuratov, Mikhail Burtsev
arXiv:2606. 08102v1 Announce Type: cross Abstract: Multi-quadruped coordination has attracted increasing attention due to its enhanced payload capacity, broader contact coverage, and improved adaptability to challenging tasks.
By Daoqing Wang, Yuchen Xiao, Weixuan Huang, Zhilong Zhang, Shenghua Wan, Meng Li, Lei Yuan, Yang Yu
arXiv:2609.15361v1 Announce Type: cross
Abstract: Effective communication is a cornerstone of distributed intelligence in Multi-Agent Reinforcement Learning (MARL), yet ensuring that generated messag...
By Rafael Pina, Varuna De Silva, Corentin Artaud
arXiv:2503. 22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition.
By Puzhen Yuan, Angyuan Ma, Yunchao Yao, Huaxiu Yao, Masayoshi Tomizuka, Mingyu Ding
arXiv:2510. 14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully.
By Jinrui Liu, Bingyan Nie, Boyu Li, Yaran Chen, Yuze Wang, Shunsen He, Haoran Li
arXiv:2609.36588v1 Announce Type: cross
Abstract: We study reinforcement learning (RL) methods for cooperative multi-agent Vision-Language-Action (VLA) models. This problem is challenging because VLA...
By Ruixiao Xu, Wong Lik Hang Kenny, Zhiqian Liu, Jianing Guo, Hanxiao Li, Kejian Shi, Shuning Zhang, Pu Feng, Yongjia Ma, Yuqing Ma, Kai Chen, Qi Dou, Yaodong Yang, Xianglong Liu, Simin Li
arXiv:2606. 08992v1 Announce Type: cross Abstract: Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in order to follow language instructions.
By Yucheng Deng, Pingrui Lai, Xinhai Li, Chenjia Bai, Xiaoheng Deng, Chengnuo Sun, Xuelong Li, Hua Yang
arXiv:2610.14248v1 Announce Type: cross
Abstract: Diffusion models provide expressive trajectory priors for multi-robot motion planning (MRMP), but scaling them to large teams requires coordinating i...
By Manav Vora, Gokul Puthumanaillam, Aditya Penumarti, Jane Shin, Melkior Ornik
arXiv:2603.08814v2 Announce Type: replace-cross
Abstract: Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; y...
By Piyush Gupta, Sangjae Bae, Jiachen Li, David Isele