arXiv AI
VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Oct 5
Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning
The paper introduces Bidirectional Voronoi-biased Exploration Curriculum (BVER), a method that expands start states from the goal and goals from the initial state simultaneously, guiding both toward each other to train a single goal-conditioned policy. Inspired by bidirectional RRT planning, BVER biases exploration toward unexplored task space and steers the two expansions together. Experiments on point-mass mazes, quadrupedal box climbing, and robot-arm ring-on-peg transfer show that BVER learns faster than other reference-free curricula, achieving high success rates and robustness without requiring demonstrations.
arXiv Machine Learning
Aug 28
Diffusion Policies for Short-Horizon Planning in Robot Crowd Navigation
The paper introduces Planning Diffusion Policy Optimization (PDPO), an offline‑to‑online reinforcement‑learning framework that employs a diffusion policy to produce short‑horizon action chunks for robot crowd navigation. PDPO is pretrained on collision‑avoidance demonstrations and fine‑tuned online with PPO, generating five‑step action sequences applied in a receding‑horizon manner. The authors also identify a benchmark artifact where agents can leave the valid domain without explicit boundary constraints, and they mitigate this by treating boundary violations as collisions, leading to improved success rates over strong baselines.
arXiv AI
Aug 7
Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations
arXiv:2608. 05588v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones.
arXiv Machine Learning
Aug 13
Adaptation of Generalist Robot Policies with Minimal Data
arXiv:2608. 11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction.
arXiv Machine Learning
Oct 21
Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration
The paper introduces Hybrid Belief Reinforcement Learning (HBRL), a two‑phase framework for coordinating multiple autonomous agents in spatial exploration tasks. In phase one, agents build spatial beliefs via a Log‑Gaussian Cox Process and plan information‑driven trajectories using a Pathwise Mutual Information planner; in phase two, a Soft Actor‑Critic policy is warm‑started through dual‑channel knowledge transfer that incorporates belief states and replay‑buffer demonstrations. Experiments on UAV wireless service provisioning and NYC taxi demand sensing show significant performance gains, with the dual‑channel transfer outperforming single‑channel approaches.
arXiv Machine Learning
Oct 28
RBGA: Residual-Budget Guide Arbitration for Offline-to-Online Safe Reinforcement Learning in Dexterous Manipulation
The paper introduces RBGA, an algorithm for offline-to-online safe reinforcement learning in dexterous manipulation. RBGA tracks the remaining safety budget during an episode and uses it to arbitrate between offline guidance and an evolving online policy, comparing cost-to-go estimates at each step. The authors provide theoretical analysis of safety implications and demonstrate through experiments that RBGA improves online policy performance while preserving safety.