arXiv Machine Learning
Latent Telepathy: Multi-Robot Communication with Self-Supervised Perceptual Latents
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arXiv Machine Learning
Jul 23
Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning
arXiv:2607. 19809v1 Announce Type: cross Abstract: In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability.
arXiv AI
Sep 15
Robust and Efficient Communication for Multi-Agent Learning
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...
arXiv AI
4d ago
Cooperative Multi-Agent Vision-Language-Action Models via Reinforced Fine Tuning
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...
arXiv AI
Aug 25
SRMT: Shared Memory for Multi-agent Lifelong Pathfinding
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.
arXiv AI
Sep 11
Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G
The paper introduces FedMVLA, a modality‑decoupled federated learning framework designed for privacy‑preserving embodied intelligence in 6G networks. It addresses the unique challenges of vision‑language‑action models by employing modality‑aware federated aggregation, privacy allocation, and communication compression, along with a precision‑critical action transport slice. A case study on federated robotic manipulation demonstrates significant gains in task success, scalability, and uplink payload reduction compared to standard FedAvg.
Hugging Face Trending Papers
Sep 9
Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G
Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots collaborate through low-latency connectivity, edg...