96 citations · 359 across the 71 of their papers we have counts for
10 papers · 1 filter
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…
Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty
Ajinkya Jain, Scott Niekum
Sudden changes in the dynamics of robotic tasks, such as contact with an object or the latching of a door, are often viewed as inconvenient discontinuities that make manipulation d…
Understanding Teacher Gaze Patterns for Robot Learning
Akanksha Saran, Elaine Schaertl Short, Andrea Thomaz +1
Human gaze is known to be a strong indicator of underlying human intentions and goals during manipulation tasks. This work studies gaze patterns of human teachers demonstrating tas…
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations
Daniel S. Brown, Wonjoon Goo, Scott Niekum
The performance of imitation learning is typically upper-bounded by the performance of the demonstrator. While recent empirical results demonstrate that ranked demonstrations allow…
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Oliver Kroemer, Scott Niekum, George Konidaris
A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen subs…
Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning
Caleb Chuck, Supawit Chockchowwat, Scott Niekum
Deep reinforcement learning (DRL) is capable of learning high-performing policies on a variety of complex high-dimensional tasks, ranging from video games to robotic manipulation.…
Uncertainty-Aware Data Aggregation for Deep Imitation Learning
Yuchen Cui, David Isele, Scott Niekum +1
Estimating statistical uncertainties allows autonomous agents to communicate their confidence during task execution and is important for applications in safety-critical domains suc…
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…
Using Natural Language for Reward Shaping in Reinforcement Learning
Prasoon Goyal, Scott Niekum, Raymond J. Mooney
Recent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient. A common approach to re…
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…