Facilitating intention prediction for humans by optimizing robot motions

Members of a team are able to coordinate their actions by anticipating the intentions of others. Achieving such implicit coordination between humans and robots requires humans to be able to quickly and robustly predict the robot's intentions, i.e. the robot should demonstrate a behavior that is legible. Whereas previous work has sought to explicitly optimize the legibility of behavior, we investigate legibility as a property that arises automatically from general requirements on the efficiency and robustness of joint human-robot task completion. We do so by optimizing fast and successful completion of joint human-robot tasks through policy improvement with stochastic optimization. Two experiments with human subjects show that robots are able to adapt their behavior so that humans become better at predicting the robot's intentions early on, which leads to faster and more robust overall task completion.

[1]  Olivier Sigaud,et al.  Policy Improvement Methods: Between Black-Box Optimization and Episodic Reinforcement Learning , 2012 .

[2]  Martin Buss,et al.  Human-Robot Collaboration: a Survey , 2008, Int. J. Humanoid Robotics.

[3]  Alois Knoll,et al.  Interacting in time and space: Investigating human-human and human-robot joint action , 2010, 19th International Symposium in Robot and Human Interactive Communication.

[4]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[5]  Siddhartha S. Srinivasa,et al.  Predictability or adaptivity? Designing robot handoffs modeled from trained dogs and people , 2011, 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[6]  Michael Beetz,et al.  Implicit coordination in robotic teams using learned prediction models , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[7]  W. Erlhagen,et al.  On the development of intention understanding for joint action tasks , 2007, 2007 IEEE 6th International Conference on Development and Learning.

[8]  Siddhartha S. Srinivasa,et al.  Generating Legible Motion , 2013, Robotics: Science and Systems.

[9]  Jan Peters,et al.  Noname manuscript No. (will be inserted by the editor) Policy Search for Motor Primitives in Robotics , 2022 .

[10]  Jun Nakanishi,et al.  Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors , 2013, Neural Computation.

[11]  U. Castiello,et al.  Cues to intention: The role of movement information , 2011, Cognition.

[12]  Siddhartha S. Srinivasa,et al.  Legibility and predictability of robot motion , 2013, 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[13]  V. Manera,et al.  Grasping intentions: from thought experiments to empirical evidence , 2012, Front. Hum. Neurosci..

[14]  H. Bekkering,et al.  Joint action: bodies and minds moving together , 2006, Trends in Cognitive Sciences.

[15]  Siddhartha S. Srinivasa,et al.  Using spatial and temporal contrast for fluent robot-human hand-overs , 2011, 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[16]  Siddhartha S. Srinivasa,et al.  Toward seamless human-robot handovers , 2013, Journal of Human-Robot Interaction.

[17]  Cordula Vesper,et al.  Making oneself predictable: reduced temporal variability facilitates joint action coordination , 2011, Experimental Brain Research.

[18]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[19]  Enrico Pagello,et al.  Cooperative behaviors in multi-robot systems through implicit communication , 1999, Robotics Auton. Syst..

[20]  Siddhartha S. Srinivasa,et al.  Learning the communication of intent prior to physical collaboration , 2012, 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication.

[21]  Freek Stulp,et al.  Simultaneous on-line Discovery and Improvement of Robotic Skill options , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.