Learning torque control in presence of contacts using tactile sensing from robot skin
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[1] Jan Peters,et al. Model learning for robot control: a survey , 2011, Cognitive Processing.
[2] Giulio Sandini,et al. An embedded artificial skin for humanoid robots , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.
[3] Jan Peters,et al. Learning inverse dynamics models with contacts , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[4] Gentiane Venture,et al. Dynamic parameters identification of a humanoid robot using joint torque sensors and/or contact forces , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.
[5] Giulio Sandini,et al. Approximate optimal control for reaching and trajectory planning in a humanoid robot , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[6] Serena Ivaldi,et al. Inertial parameters identification and joint torques estimation with proximal force/torque sensing , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[7] Jan Peters,et al. Bayesian Gait Optimization for Bipedal Locomotion , 2014, LION.
[8] Advait Jain,et al. Manipulation in Clutter with Whole-Arm Tactile Sensing , 2013, ArXiv.
[9] Fulvio Mastrogiovanni,et al. Skin spatial calibration using force/torque measurements , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[10] Giulio Sandini,et al. The iCub Platform: A Tool for Studying Intrinsically Motivated Learning , 2013, Intrinsically Motivated Learning in Natural and Artificial Systems.
[11] Daniele Pucci,et al. iCub Whole-Body Control through Force Regulation on Rigid Non-Coplanar Contacts , 2015, Front. Robot. AI.
[12] Carl E. Rasmussen,et al. Gaussian Processes for Data-Efficient Learning in Robotics and Control , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Vincent Padois,et al. Emergence of humanoid walking behaviors from mixed-integer model predictive control , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[14] Duy Nguyen-Tuong,et al. Local Gaussian Process Regression for Real Time Online Model Learning , 2008, NIPS.
[15] Giorgio Metta,et al. Control of contact forces: The role of tactile feedback for contact localization , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[16] Jan Peters,et al. Toward fast policy search for learning legged locomotion , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[17] G. Oriolo,et al. Robotics: Modelling, Planning and Control , 2008 .
[18] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[19] Emanuel Todorov,et al. Trajectory optimization for domains with contacts using inverse dynamics , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[20] Advait Jain,et al. Reaching in clutter with whole-arm tactile sensing , 2013, Int. J. Robotics Res..
[21] Katsu Yamane,et al. Practical kinematic and dynamic calibration methods for force-controlled humanoid robots , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.
[22] Stefan Schaal,et al. Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space , 2000, ICML.
[23] Giulio Sandini,et al. Computing robot internal/external wrenches by means of inertial, tactile and F/T sensors: Theory and implementation on the iCub , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.
[24] Lorenzo Jamone,et al. Incremental learning of context-dependent dynamic internal models for robot control , 2014, 2014 IEEE International Symposium on Intelligent Control (ISIC).