3 citations · 4 across the 2 of their papers we have counts for
12 papers
Learning a Shield from Catastrophic Action Effects: Never Repeat the Same Mistake
Shahaf S. Shperberg, Bo Liu, Peter Stone
Agents that operate in an unknown environment are bound to make mistakes while learning, including, at least occasionally, some that lead to catastrophic consequences. When humans…
Team Orienteering Coverage Planning with Uncertain Reward
Bo Liu, Xuesu Xiao, Peter Stone
Many municipalities and large organizations have fleets of vehicles that need to be coordinated for tasks such as garbage collection or infrastructure inspection. Motivated by this…
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition
Bo Liu, Qiang Liu, Peter Stone +3
In real-world multi-agent systems, agents with different capabilities may join or leave without altering the team's overarching goals. Coordinating teams with such dynamic composit…
APPLI: Adaptive Planner Parameter Learning From Interventions
Zizhao Wang, Xuesu Xiao, Bo Liu +2
While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptima…
APPLR: Adaptive Planner Parameter Learning from Reinforcement
Zifan Xu, Gauraang Dhamankar, Anirudh Nair +5
Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-t…
Extended Abstract: Motion Planners Learned from Geometric Hallucination
Xuesu Xiao, Bo Liu, Peter Stone
Learning motion planners to move robot from one point to another within an obstacle-occupied space in a collision-free manner requires either an extensive amount of data or high-qu…