Learning from Humans—Computational Models of Cognition-Enabled Control of Everyday Activity

In recent years, we have seen tremendous advances in the mechatronic, sensing and computational infrastructure of robots, enabling them to act in several application domains faster, stronger and more accurately than humans do. Yet, when it comes to accomplishing manipulation tasks in everyday settings, robots often do not even reach the sophistication and performance of young children. In this article, we describe an interdisciplinary research approach in which we design computational models for controlling robots performing everyday manipulation tasks inspired by the observation of human activities.

[1]  Marc Toussaint,et al.  Probabilistic inference as a model of planned behavior , 2009, Künstliche Intell..

[2]  三嶋 博之.  The theory of affordances , 2008 .

[3]  Gerald J. Sussman,et al.  A Computational Model of Skill Acquisition , 1973 .

[4]  Ian Horswill.  Integrated systems and naturalistic tasks , 1996, CSUR.

[5]  Michael Beetz,et al.  How Humans Optimize Their Interaction with the Environment: The Impact of Action Context on Human Perception , 2009, FIRA.

[6]  Moritz Tenorth,et al.  Towards performing everyday manipulation activities , 2010, Robotics Auton. Syst..

[7]  Michael Beetz,et al.  Accurate Human Motion Capture Using an Ergonomics-Based Anthropometric Human Model , 2008, AMDO.

[8]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

[9]  Moritz Tenorth,et al.  KNOWROB — knowledge processing for autonomous personal robots , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Moritz Tenorth,et al.  Towards Automated Models of Activities of Daily Life , 2009 .

[11]  Michael Beetz,et al.  Obstacle avoidance in a pick-and-place task , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[12]  Giulio Sandini,et al.  A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents , 2007, IEEE Transactions on Evolutionary Computation.

[13]  Michael Beetz,et al.  Compact models of motor primitive variations for predictable reaching and obstacle avoidance , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[14]  Michael Beetz,et al.  Generality and legibility in mobile manipulation , 2010, Auton. Robots.

[15]  Bernhard Nebel,et al.  On the Computational Complexity of Temporal Projection, Planning, and Plan Validation , 1994, Artif. Intell..

[16]  Zoubin Ghahramani,et al.  Computational principles of movement neuroscience , 2000, Nature Neuroscience.

[17]  E. Todorov.  Optimality principles in sensorimotor control , 2004, Nature Neuroscience.