3 accepted papers
Multi-arm motion planning is fundamental for enabling arms to complete collaborative tasks in shared spaces but current methods struggle with scalability due to exponential state-space growth and reliance on large training datasets for learned models. Inspired by Multi-Agent Path Finding (MAPF), whi…
Safe navigation is essential for autonomous systems operating in hazardous environments. Traditional planning methods are effective for solving long-horizon tasks but depend on the availability of a graph representation with prede-fined distance metrics. In contrast, safe Reinforcement Learning (RL)
Scientists often search for phenomenon of interest while exploring new environments. Autonomous vehicles are deployed to explore such areas where human-operated vehicles would be costly or dangerous. Online control of autonomous vehicles for information-gathering is called adaptive sampling and can…