96 citations · 359 across the 71 of their papers we have counts for
6 papers · 1 filter
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…
Towards Online Learning from Corrective Demonstrations
Reymundo A. Gutierrez, Elaine Schaertl Short, Scott Niekum +1
Robots operating in real-world human environments will likely encounter task execution failures. To address this, we would like to allow co-present humans to refine the robot's tas…
One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video
Wonjoon Goo, Scott Niekum
Due to burdensome data requirements, learning from demonstration often falls short of its promise to allow users to quickly and naturally program robots. Demonstrations are inheren…
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
Josiah P. Hanna, Scott Niekum, Peter Stone
We consider the problem of off-policy evaluation in Markov decision processes. Off-policy evaluation is the task of evaluating the expected return of one policy with data generated…
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
Daniel S. Brown, Scott Niekum
Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and generalization. However, despite much recent interest in IRL,…
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
Ajinkya Jain, Scott Niekum
Noisy observations coupled with nonlinear dynamics pose one of the biggest challenges in robot motion planning. By decomposing nonlinear dynamics into a discrete set of local dynam…