A flexible hybrid framework for modeling complex manipulation tasks

Future service robots will need to perform a wide range of tasks using various objects. In order to perform complex tasks, robots require a suitable internal representation of the task. We propose a hybrid framework for representing manipulation tasks, which combines continuous motion planning and discrete task-level planning. In addition, we use a mid-level planner to optimize individual actions according to the plan. The proposed framework incorporates biologically-inspired concepts, such as affordances and motor primitives, in order to efficiently plan for manipulation tasks. The final framework is modular, can generalize well to different situations, and is straightforward to expand. Our demonstrations also show how the use of affordances and mid-level planning can lead to improved performance.

[1]  E. Reed.  The Ecological Approach to Visual Perception , 1989 .

[2]  Thomas A. Henzinger,et al.  The theory of hybrid automata , 1996, Proceedings 11th Annual IEEE Symposium on Logic in Computer Science.

[3]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[4]  Christos G. Cassandras,et al.  Introduction to Discrete Event Systems , 1999, The Kluwer International Series on Discrete Event Dynamic Systems.

[5]  Reza Shadmehr,et al.  Learning of action through adaptive combination of motor primitives , 2000, Nature.

[6]  Jun Nakanishi,et al.  Learning Attractor Landscapes for Learning Motor Primitives , 2002, NIPS.

[7]  Giulio Sandini,et al.  Learning about objects through action - initial steps towards artificial cognition , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[8]  Jun Nakanishi,et al.  Learning Movement Primitives , 2005, ISRR.

[9]  G. Rizzolatti,et al.  The mirror-neuron system. , 2004, Annual review of neuroscience.

[10]  G. Rizzolatti,et al.  Parietal Lobe: From Action Organization to Intention Understanding , 2005, Science.

[11]  Michael S. Branicky,et al.  Introduction to Hybrid Systems , 2005, Handbook of Networked and Embedded Control Systems.

[12]  Tamar Flash,et al.  Motor primitives in vertebrates and invertebrates , 2005, Current Opinion in Neurobiology.

[13]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[14]  Miles C. Bowman,et al.  Control strategies in object manipulation tasks , 2006, Current Opinion in Neurobiology.

[15]  Gordon Cheng,et al.  A computational model of anterior intraparietal (AIP) neurons , 2006, Neurocomputing.

[16]  Maya Cakmak,et al.  To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control , 2007, Adapt. Behav..

[17]  Petrisor Anca,et al.  On hybrid systems modeling with Petri nets , 2008 .

[18]  Danica Kragic,et al.  Birth of the Object: Detection of Objectness and Extraction of Object Shape through Object-Action complexes , 2008, Int. J. Humanoid Robotics.

[19]  J. Randall Flanagan,et al.  Coding and use of tactile signals from the fingertips in object manipulation tasks , 2009, Nature Reviews Neuroscience.

[20]  J. Hermsdörfer,et al.  Grasping tools: Effects of task and apraxia , 2009, Neuropsychologia.

[21]  Jivko Sinapov,et al.  Toward interactive learning of object categories by a robot: A case study with container and non-container objects , 2009, 2009 IEEE 8th International Conference on Development and Learning.

[22]  Andrew G. Barto,et al.  Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining , 2009, NIPS.

[23]  Manuel Lopes,et al.  Learning grasping affordances from local visual descriptors , 2009, 2009 IEEE 8th International Conference on Development and Learning.

[24]  Jan Peters,et al.  Imitation and Reinforcement Learning , 2010, IEEE Robotics & Automation Magazine.

[25]  J. Peters,et al.  Imitation and Reinforcement Learning â Practical Algorithms for Motor Primitive Learning in Robotics , 2010 .

[26]  Jan Peters,et al.  Imitation and Reinforcement Learning: Practical Algorithms for Motor Primitives in Robotics , 2010 .

[27]  Oliver Kroemer,et al.  Combining active learning and reactive control for robot grasping , 2010, Robotics Auton. Syst..

[28]  Ulrich Hillenbrand.  Non-parametric 3D Shape Warping , 2010, 2010 20th International Conference on Pattern Recognition.

[29]  Jan Peters,et al.  Reinforcement Learning to Adjust Robot Movements to New Situations , 2010, IJCAI.