Human-Interactive Robot Learning: Definition, Challenges, and Recommendations
Kim Baraka(Vrije Universiteit Amsterdam), Xuesu Xiao(George Mason University), Helen Beierling(Bielefeld University), Jens Kober(University of Stuttgart), Anna-Lisa Vollmer(Bielefeld University), Antonio Andriella(Institut de Robòtica i Informàtica Industrial), Mohamed Chétouani(Inserm), Taylor Kessler Faulkner(University of Washington), Akanksha Saran(Sony Corporation (United States)), Emmanuel Senft(Idiap Research Institute), Isaac Sheidlower(Tufts University), Matthew E. Taylor(University of Alberta), Ifrah Idrees(John Brown University), Serena Booth(John Brown University), Daniel H. Grollman(Robotics Research (United States)), Sanne van Waveren(Georgia Institute of Technology), Silvia Tulli(Centre National de la Recherche Scientifique), Tiffany Horter(University of Oxford), Erdem Bıyık(University of Southern California)
Cited by 1
Related Papers
Reinforcement learning in robotics: A survey
|The International Journal of Robotics Research|2013|3.1k
Reinforcement learning for control: Performance, stability, and deep approximators
|Annual Reviews in Control|2018|452
Policy search for motor primitives in robotics
|Machine Learning|2010|438
Learning to select and generalize striking movements in robot table tennis
|The International Journal of Robotics Research|2013|404
Relative Entropy Inverse Reinforcement Learning
|Max Planck Digital Library|2011|257