A bottom-up integration of vision and actions to create cognitive humanoids

In recent years more and more complex humanoid robots have been developed. On the other hand programming these systems has become more difficult. There is a clear need for such robots to be able to adapt and perform certain tasks autonomously, or even learn by themselves how to act. An important issue to tackle is the closing of the sensorimotor loop. Especially when talking about humanoids the tight integration of perception with actions will allow for improved behaviours, embedding adaptation on the lower-level of the system.

[1]  A. Meltzoff.  Infant Imitation After a 1-Week Delay: Long-Term Memory for Novel Acts and Multiple Stimuli. , 1988, Developmental psychology.

[2]  Danica Kragic,et al.  Vision for Robotics , 2009, Found. Trends Robotics.

[3]  Jürgen Leitner,et al.  Autonomous learning of robust visual object detection and identification on a humanoid , 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

[4]  Zoubin Ghahramani,et al.  Perspectives and problems in motor learning , 2001, Trends in Cognitive Sciences.

[5]  Stefano Nolfi,et al.  Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines , 2000 .

[6]  V. Gullapalli,et al.  Visual Information and Object Size in the Control of Reaching. , 1996, Journal of motor behavior.

[7]  Giulio Sandini,et al.  Developmental robotics: a survey , 2003, Connect. Sci..

[8]  Juyang Weng,et al.  Developmental Robotics: Theory and Experiments , 2004, Int. J. Humanoid Robotics.

[9]  Larry H. Matthies,et al.  End-to-end dexterous manipulation with deliberate interactive estimation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[10]  Ivan Bratko,et al.  Automated Chess Tutor , 2006, Computers and Games.

[11]  Pieter Abbeel,et al.  Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding , 2010, 2010 IEEE International Conference on Robotics and Automation.

[12]  L. Munari.  How the body shapes the way we think — a new view of intelligence , 2009 .

[13]  Rodney A. Brooks,et al.  Cambrian Intelligence: The Early History of the New AI , 1999 .

[14]  Giorgio Metta,et al.  Ensuring safety of policies learned by reinforcement: Reaching objects in the presence of obstacles with the iCub , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Salvatore Gaglio,et al.  A cognitive architecture for robot self-consciousness , 2008, Artif. Intell. Medicine.

[16]  G. Sandini,et al.  The iCub cognitive architecture: Interactive development in a humanoid robot , 2007, 2007 IEEE 6th International Conference on Development and Learning.

[17]  Jürgen Leitner,et al.  Improving robot vision models for object detection through interaction , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[18]  Bernd Dachwald,et al.  Optimization of interplanetary solar sailcraft trajectories using evolutionary neurocontrol , 2004 .

[19]  M. E. McCarty,et al.  How infants use vision for grasping objects. , 2001, Child development.

[20]  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.

[21]  Jürgen Leitner,et al.  Evolving ANNs for Spacecraft Rendezvous and Docking , 2010 .

[22]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[23]  Inman Harvey,et al.  Evolutionary robotics: the Sussex approach , 1997, Robotics Auton. Syst..

[24]  Mikhail Frank,et al.  Learning spatial object localization from vision on a humanoid robot , 2012 .

[25]  M. Shanahan.  A cognitive architecture that combines internal simulation with a global workspace , 2006, Consciousness and Cognition.

[26]  Mark H. Johnson,et al.  Processes of change in brain and cognitive development , 2005, Trends in Cognitive Sciences.

[27]  Stefano Nolfi,et al.  How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics , 1994 .

[28]  Masaki Ogino,et al.  Cognitive Developmental Robotics: A Survey , 2009, IEEE Transactions on Autonomous Mental Development.

[29]  M. Arbib,et al.  Infant grasp learning: a computational model , 2004, Experimental Brain Research.

[30]  Jürgen Leitner,et al.  Cartesian Genetic Programming for Image Processing , 2013 .

[31]  Nikolaos G. Tsagarakis,et al.  iCub: the design and realization of an open humanoid platform for cognitive and neuroscience research , 2007, Adv. Robotics.

[32]  M. Posner.  Foundations of cognitive science , 1989 .

[33]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[34]  Ashutosh Saxena,et al.  Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..

[35]  Jürgen Leitner,et al.  Transferring spatial perception between robots operating in a shared workspace , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[36]  Karl Iagnemma,et al.  Design and Analysis of a Robust, Low-cost, Highly Articulated manipulator enabled by jamming of granular media , 2012, 2012 IEEE International Conference on Robotics and Automation.

[37]  Jürgen Leitner,et al.  Task-relevant roadmaps: A framework for humanoid motion planning , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[38]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[39]  R. Johansson,et al.  Development of human precision grip I: Basic coordination of force , 2004, Experimental Brain Research.

[40]  Stefan Schaal,et al.  Reinforcement Learning for Humanoid Robotics , 2003 .

[41]  Minoru Asada,et al.  Cognitive developmental robotics as a new paradigm for the design of humanoid robots , 2001, Robotics Auton. Syst..

[42]  Nick Hawes,et al.  Multiple Workspaces as an Architecture for Cognition , 2008, AAAI Fall Symposium: Biologically Inspired Cognitive Architectures.

[43]  Giulio Sandini,et al.  The ITALK project: Integration and Transfer of Action and Language Knowledge in Robots , 2008 .

[44]  Jürgen Leitner,et al.  Curiosity driven reinforcement learning for motion planning on humanoids , 2014, Front. Neurorobot..

[45]  Peter C. Chu.  SMART underwater robot (SUR) application and mining , 2011 .

[46]  M. Jeannerod.  The cognitive neuroscience of action , 1997, Trends in Cognitive Sciences.

[47]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[49]  R. A. Brooks,et al.  Intelligence without Representation , 1991, Artif. Intell..

[50]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[51]  Alexander Hornberg.  Handbook of Machine Vision , 2006 .

[52]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[53]  James U. Korein,et al.  Robotics , 2018, IBM Syst. J..

[54]  Heinrich M. Jaeger,et al.  Universal robotic gripper based on the jamming of granular material , 2010, Proceedings of the National Academy of Sciences.

[55]  Hod Lipson,et al.  Resilient Machines Through Continuous Self-Modeling , 2006, Science.

[56]  Jürgen Leitner,et al.  The Modular Behavioral Environment for Humanoids and other Robots (MoBeE) , 2012, ICINCO.

[57]  Giorgio Metta,et al.  YARP: Yet Another Robot Platform , 2006 .

[58]  Inman Harvey,et al.  Explorations in Evolutionary Robotics , 1993, Adapt. Behav..

[59]  Charles C. Kemp,et al.  Challenges for robot manipulation in human environments [Grand Challenges of Robotics] , 2007, IEEE Robotics & Automation Magazine.

[60]  Giulio Sandini,et al.  The iCub humanoid robot: An open-systems platform for research in cognitive development , 2010, Neural Networks.