14 citations · 14 across the 3 of their papers we have counts for
5 papers
MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation
Aaron M. Roth, Jing Liang, Ram Sriram +2
We present Multiple Scenario Verifiable Reinforcement Learning via Policy Extraction (MSVIPER), a new method for policy distillation to decision trees for improved robot navigation…
XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees
Aaron M. Roth, Jing Liang, Dinesh Manocha
We present a novel sensor-based learning navigation algorithm to compute a collision-free trajectory for a robot in dense and dynamic environments with moving obstacles or targets.…
A Robot's Expressive Language Affects Human Strategy and Perceptions in a Competitive Game
Aaron M. Roth, Samantha Reig, Umang Bhatt +5
As robots are increasingly endowed with social and communicative capabilities, they will interact with humans in more settings, both collaborative and competitive. We explore human…
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy
Aaron M. Roth, Nicholay Topin, Pooyan Jamshidi +1
There is a growing desire in the field of reinforcement learning (and machine learning in general) to move from black-box models toward more "interpretable AI." We improve interpre…
The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting
Aaron M. Roth, Umang Bhatt, Tamara Amin +3
With the rapid development of robot and other intelligent and autonomous agents, how a human could be influenced by a robot's expressed mood when making decisions becomes a crucial…