90 citations · 230 across the 45 of their papers we have counts for
7 papers · 1 filter
APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback
Zizhao Wang, Xuesu Xiao, Garrett Warnell +1
Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however,…
Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks
Ruohan Zhang, Faraz Torabi, Garrett Warnell +1
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is…
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning
Eddy Hudson, Garrett Warnell, Peter Stone
While Adversarial Imitation Learning (AIL) algorithms have recently led to state-of-the-art results on various imitation learning benchmarks, it is unclear as to what impact variou…
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation
Haresh Karnan, Garrett Warnell, Xuesu Xiao +1
While imitation learning for vision based autonomous mobile robot navigation has recently received a great deal of attention in the research community, existing approaches typicall…
APPL: Adaptive Planner Parameter Learning
Xuesu Xiao, Zizhao Wang, Zifan Xu +5
While current autonomous navigation systems allow robots to successfully drive themselves from one point to another in specific environments, they typically require extensive manua…
Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch
Eddy Hudson, Garrett Warnell, Faraz Torabi +1
Learning from demonstrations in the wild (e.g. YouTube videos) is a tantalizing goal in imitation learning. However, for this goal to be achieved, imitation learning algorithms mus…
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation
Faraz Torabi, Garrett Warnell, Peter Stone
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the contr…