3 accepted papers
In the final “Last-Centimeter” phase of manipulation, visual occlusion and calibration errors often make vision unreliable, causing robots to suffer high failure rates due to local pose uncertainty and deviations between simulated and real-world dynamics. To address these challenges, this paper prop
We investigate the Combined Target-Assignment and Path-Finding (TAPF) problem that computes both task assignments and collision-free paths for multiple agents, that is, each agent is required to select a target from an underlying set, reaching which leads to a payoff. There is a cost closely related
The Multi-Agent Combinatorial Path Finding (MCPF) problem is a generalized version of the Multi-Agent Path Finding (MAPF) problem, in which each agent must collectively visit multiple intermediate target locations on the way to its final destination. The state-of-the-art approach for addressing MCPF