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I'm currently a graduate student studying robotics and artificial intelligence at the University of Texas at Austin in the Department of Computer Science. I'm a member of the Learning Agents Research Group, which is led by my supervising professor, Peter Stone. I'm also an active member of the Reinforcement Learning Reading Group and the Agents that Learn from Humans Reading Group. My primary research interest is robotic learning, especially investigating ways in which humans can transfer knowledge to robots. More generally, I'm interested in reinforcement learning, multiagent systems, and learning from demonstration. I have been the Motorola Endowed Scholar for the past two years, which has has enabled me to fully focus my efforts on research.

publications

Adam Setapen, Michael Quinlan, and Peter Stone. MARIOnET: Motion Acquisition for Robots through Iterative Online Evaluative Training (Extended Abstract). In The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, May 2010.
Supplemental video cited in the paper.
AAMAS 2010
BibTeX Download: [pdf] (321.6kB) [ps] (4.2MB)

Adam Setapen, Michael Quinlan, and Peter Stone. Beyond Teleoperation: Exploiting Human Motor Skills with MARIOnET. To appear, 2010 AAMAS Workshop on Agents Learning Interactively from Human Teachers, Toronto, Canada, May 2010.
Supplemental video cited in the paper.
AAMAS 2010 ALIHT Workshop
Download: [pdf]

undergraduate honors thesis

Title: Exploiting Human Motor Skills for Training Bipedal Robots

Abstract: Although machine learning, reinforcement learning, and learning from demonstration have improved the rate and accuracy at which robots can gain intelligence from humans, they haven't reached the rapid rate at which humans are able to acquire new knowledge. Many systems that exploit imitation learning use simple positive and negative reinforcement, and place the burden of learning completely on the computer. This neglects the expressive capabilities of humans, as well as their remarkable ability to quickly refine motor skills. While passive dynamics offers the most human-like locomotion for bipedal robots, it also relies on particular design specifications. This thesis presents a general Framework for Interactive Control of a Humanoid by Motion Capture (FICHMC), that offers rapid motion development for large classes of bipedal robots. Essentially, a human in a motion-capture laboratory "puppets" a biped, with a real-time mapping from human to robot. The training process requires no technical knowledge and provides a natural interface for humans to directly transfer skills to robots.

Complete Paper: PDF

Video: Hi-Res | Low-Res

interesting projects

A bootable x86 operating system I developed with my good friend Jose Falcon for my undergraduate operating systems course.

  • Language: C
  • Requirements: x86 processor, or an emulator (such as bochs)
  • Download

A pipelined processor designed and implemented (using an extended version of the LC-3 architecture) for my undergraduate computer architecture course with Daniel Chimene.

  • Language: Verilog
  • Requirements: Verilog simulator (such as VCS)
  • Download

A genetic algorithm for learning to play keepaway in the robotic soccer domain. I designed this algorithm in my undergraduate course, Autonomous Multiagent Systems.

Simultaneous Localization and Mapping (SLAM) simulator designed in my graduate robotics course. Displays a map of a mobile robot's probabilistic position in its environment using a particle filter.

A monadic parser, typechecker, and evaluator for a simply-typed lambda calculus augmented with booleans, natural numbers, fix-points and references.

An implementation of the RSA encryption/decription protocol.