most citedLearn what matters: cross-domain imitation learning with task-relevant embeddings

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2024

Select to Perfect: Imitating desired behavior from large multi-agent data

Tim Franzmeyer, Edith Elkind, Philip Torr +2

AI agents are commonly trained with large datasets of demonstrations of human behavior. However, not all behaviors are equally safe or desirable. Desired characteristics for an AI…

cs.AI2022★ 2 cited

Learn what matters: cross-domain imitation learning with task-relevant embeddings

Tim Franzmeyer, Philip H. S. Torr, João F. Henriques

We study how an autonomous agent learns to perform a task from demonstrations in a different domain, such as a different environment or different agent. Such cross-domain imitation…