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Found 5 papers, 1 papers with code
Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning
Effective and efficient task planning is essential for mobile robots, especially in applications like warehouse retrieval and environmental monitoring.
SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning
Our experimental results indicate that SATA demonstrates remarkable compliance and safety, even in challenging environments such as soft/slippery terrain or narrow passages, and under significant external disturbances, highlighting its potential for practical deployments in human-centric and safety-critical scenarios.
SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding
To tackle this challenge, we introduce a new framework that applies sheaf theory to decentralized deep reinforcement learning, enabling agents to learn geometric cross-dependencies between each other through local consensus and utilize them for tightly cooperative decision-making.
Distributed Reinforcement Learning for Robot Teams: A Review
This review aims to provide an analysis of the state-of-the-art in distributed MARL for multi-robot cooperation.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem
Encouraged by the recent developments in deep reinforcement learning (dRL), this work approaches the mTSP as a cooperative task and introduces DAN, a decentralized attention-based neural method that aims at tackling this key trade-off.
