Real-world robotics: Learning to plan for robust execution

In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, “proves” that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.

[1]  Tom Michael Mitchell,et al.  Explanation-based generalization: A unifying view , 1986 .

[2]  황용구,et al.  Robot path planning using a potential field representation. , 1988 .

[3]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[4]  Ernest Davis,et al.  Representing and Acquiring Geographic Knowledge , 1986 .

[5]  L. Suchman.  Plans and situated actions , 1987 .

[6]  Avinash C. Kak,et al.  Spar: A Planner that Satisfies Operational and Geometric Goals in Uncertain Environments , 1990, AI Mag..

[7]  Michael A. Erdmann,et al.  Using Backprojections for Fine Motion Planning with Uncertainty , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[8]  Tom M. Mitchell,et al.  LEAP: A Learning Apprentice for VLSI Design , 1985, IJCAI.

[9]  Rodney A. Brooks.  PLANNING IS JUST A WAY OF AVOIDING FIGURING OUT WHAT TO DO NEXT , 1987 .

[10]  Alberta Maria Segre,et al.  Machine Learning of Robot Assembly Plans , 1988 .

[11]  Kenneth D. Forbus.  Qualitative Process Theory , 1984, Artificial Intelligence.

[12]  David Chapman,et al.  Pengi: An Implementation of a Theory of Activity , 1987, AAAI.

[13]  Russell H. Taylor,et al.  Automatic Synthesis of Fine-Motion Strategies for Robots , 1984 .

[14]  King-Sun Fu,et al.  A hierarchical-orthogonal-space approach to collision-free path planning , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[15]  Gerald DeJong.  Explanation-Based Learning with Plausible Inferencing , 1990 .

[16]  Michael A. Erdmann,et al.  Using Backprojections for Fine Motion Planning with Uncertainty , 1986 .

[17]  Allen Newell,et al.  Knowledge Level Learning in Soar , 1987, AAAI.

[18]  Rodney A. Brooks,et al.  Symbolic Error Analysis and Robot Planning , 1982 .

[19]  Scott William Bennett.  Learning Uncertainty Tolerant Plans through Approximation in Complex Domains , 1989 .

[20]  David Chapman,et al.  Planning for Conjunctive Goals , 1987, Artif. Intell..

[21]  Marcel Schoppers,et al.  Universal Plans for Reactive Robots in Unpredictable Environments , 1987, IJCAI.

[22]  Sanjaya Addanki,et al.  LOGnets: A Hybrid Graph Spatial Representation for Robot Navigation , 1990, AAAI.

[23]  R. Mooney,et al.  Explanation-Based Learning: An Alternative View , 1986, Machine Learning.

[24]  R. James Firby,et al.  An Investigation into Reactive Planning in Complex Domains , 1987, AAAI.

[25]  Scott Bennett,et al.  A Domain Independent Explanation-Based Generalizer , 1986, AAAI.

[26]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[27]  Jean-Claude Latombe,et al.  Constraint reformulation in a hierarchical path planner , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[28]  Timothy M. Converse,et al.  Toward a Theory of Agency , 1993 .