Now showing items 1-13 of 13

    • Automatic Task Decomposition and State Abstraction from Demonstration 

      Cobo, Luis C.; Isbell, Charles L., Jr.; Thomaz, Andrea L. (Georgia Institute of TechnologyInternational Foundation for Autonomous Agents and Multiagent Systems, 2012-06)
      Both Learning from Demonstration (LfD) and Reinforcement Learning (RL) are popular approaches for building decision-making agents. LfD applies supervised learning to a set of human demonstrations to infer and imitate the ...
    • Controlling Social Dynamics with a Parametrized Model of Floor Regulation 

      Chao, Crystal; Thomaz, Andrea L. (Georgia Institute of TechnologyBrigham Young University, 2012)
      Turn-taking is ubiquitous in human communication, yet turn-taking between humans and robots continues to be stilted and awkward for human users. The goal of our work is to build autonomous robot controllers for successfully ...
    • Effective robot task learning by focusing on task-relevant objects 

      Lee, Kyu Hwa; Lee, Jinhan; Thomaz, Andrea L.; Bobick, Aaron F. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2009-10)
      In a robot learning from demonstration framework involving environments with many objects, one of the key problems is to decide which objects are relevant to a given task. In this paper, we analyze this problem and propose ...
    • Enhancing Interaction Through Exaggerated Motion Synthesis 

      Gielniak, Michael J.; Thomaz, Andrea L. (Georgia Institute of Technology, 2012-03)
      Other than eye gaze and referential gestures (e.g. pointing), the relationship between robot motion and observer attention is not well understood. We explore this relationship to achieve social goals, such as influencing ...
    • Generating Human-like Motion for Robots 

      Gielniak, Michael J.; Liu, C. Karen; Thomaz, Andrea L. (Georgia Institute of TechnologySage Publications, 2013-07)
      Action prediction and fluidity are key elements of human-robot teamwork. If a robot’s actions are hard to understand, it can impede fluid HRI. Our goal is to improve the clarity of robot motion by making it more humanlike. ...
    • Keyframe-based Learning from Demonstration Method and Evaluation 

      Akgun, Baris; Cakmak, Maya; Jiang, Karl; Thomaz, Andrea L. (Georgia Institute of TechnologySpringer, 2012-06)
      We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a human-robot interaction perspective. Our approach –Keyframe-based Learning from Demonstration (KLfD)– ...
    • Multi-Cue Contingency Detection 

      Lee, Jinhan; Chao, Crystal; Bobick, Aaron F.; Thomaz, Andrea L. (Georgia Institute of TechnologySpringer, 2012-04)
      The ability to detect a human's contingent response is an essential skill for a social robot attempting to engage new interaction partners or maintain ongoing turn-taking interactions. Prior work on contingency detection ...
    • Object Focused Q-Learning for Autonomous Agents 

      Cobo, Luis C.; Isbell, Charles L., Jr.; Thomaz, Andrea L. (Georgia Institute of TechnologyACM Press, 2013)
      We present Object Focused Q-learning (OF-Q), a novel reinforcement learning algorithm that can offer exponential speed-ups over classic Q-learning on domains composed of independent objects. An OF-Q agent treats the state ...
    • Policy Shaping: Integrating Human Feedback with Reinforcement Learning 

      Griffith, Shane; Subramanian, Kaushik; Scholz, Jonathan; Isbell, Charles L.; Thomaz, Andrea L. (Georgia Institute of TechnologyNeural Information Processing System, 2013)
      A long term goal of Interactive Reinforcement Learning is to incorporate non- expert human feedback to solve complex tasks. Some state-of -the-art methods have approached this problem by mapping human information to ...
    • Social Learning Mechanisms for Robots 

      Thomaz, Andrea L.; Cakmak, Maya (Georgia Institute of Technology, 2009)
      There is currently a surge of interest in service robotics—a desire to have robots leave the labs and factory floors to help solve critical issues facing our society, ranging from eldercare to education. A critical issue ...
    • Timing in Multimodal Turn-Taking Interactions: Control and Analysis Using Timed Petri Nets 

      Chao, Crystal; Thomaz, Andrea L. (Georgia Institute of TechnologyBrigham Young University, 2012)
      Turn-taking interactions with humans are multimodal and reciprocal in nature. In addition, the timing of actions is of great importance, as it influences both social and task strategies. To enable the precise control and ...
    • Towards Grounding Concepts for Transfer in Goal Learning from Demonstration 

      Chao, Crystal; Cakmak, Maya; Thomaz, Andrea L. (Georgia Institute of TechnologyInstitute of Electrical & Electronics Engineers, 2011-08)
      We aim to build robots that frame the task learning problem as goal inference so that they are natural to teach and meet people's expectations for a learning partner. The focus of this work is the scenario of a social robot ...
    • Trajectories and Keyframes for Kinesthetic Teaching: A Human-Robot Interaction Perspective 

      Akgun, Baris; Cakmak, Maya; Yoo, Jae Wook; Thomaz, Andrea L. (Georgia Institute of Technology, 2012-03)
      Kinesthetic teaching is an approach to providing demonstrations to a robot in Learning from Demonstration whereby a human physically guides a robot to perform a skill. In the common usage of kinesthetic teaching, the robot's ...