30 citations · 65 across the 3 of their papers we have counts for
4 papers
Deep Bayesian Reward Learning from Preferences
Daniel S. Brown, Scott Niekum
Bayesian inverse reinforcement learning (IRL) methods are ideal for safe imitation learning, as they allow a learning agent to reason about reward uncertainty and the safety of a l…
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan +1
A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a rewa…
Risk-Aware Active Inverse Reinforcement Learning
Daniel S. Brown, Yuchen Cui, Scott Niekum
Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query s…
LAAIR: A Layered Architecture for Autonomous Interactive Robots
Yuqian Jiang, Nick Walker, Minkyu Kim +6
When developing general purpose robots, the overarching software architecture can greatly affect the ease of accomplishing various tasks. Initial efforts to create unified robot sy…