Planning-space shift learning: Variable-space motion planning toward flexible extension of body schema
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[1] Y. Demiris,et al. From motor babbling to hierarchical learning by imitation: a robot developmental pathway , 2005 .
[2] David R. Musicant,et al. Lagrangian Support Vector Machines , 2001, J. Mach. Learn. Res..
[3] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[4] D M Wolpert,et al. Multiple paired forward and inverse models for motor control , 1998, Neural Networks.
[5] Johannes Schumacher,et al. An Introduction to Hybrid Dynamical Systems, Springer Lecture Notes in Control and Information Sciences 251 , 1999 .
[6] Rodney A. Brooks,et al. A Robust Layered Control Syste For A Mobile Robot , 2022 .
[7] Milos Hauskrecht,et al. Hierarchical Solution of Markov Decision Processes using Macro-actions , 1998, UAI.
[8] Geoffrey E. Hinton,et al. Feudal Reinforcement Learning , 1992, NIPS.
[9] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[10] Susumu Tachi,et al. A modular neural network architecture for inverse kinematics model learning , 2001, Neurocomputing.
[11] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .
[12] Yasuo Kuniyoshi,et al. Adaptive body schema for robotic tool-use , 2006, Adv. Robotics.
[13] 三嶋 博之. The theory of affordances , 2008 .
[14] M. Asada,et al. Does the invariance in multi-modalities represent the body scheme ?-a case study with vision and proprioception - , 2003 .
[15] Giulio Sandini,et al. Developmental robotics: a survey , 2003, Connect. Sci..
[16] Jun Morimoto,et al. Reinforcement learning with via-point representation , 2004, Neural Networks.
[17] Alexander Stoytchev,et al. Behavior-Grounded Representation of Tool Affordances , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.