暂无分享,去创建一个
Patrick M. Pilarski | Richard S. Sutton | Craig Sherstan | Kory Wallace Mathewson | Adam S. R. Parker | Ann L. Edwards | R. Sutton | P. Pilarski | K. Mathewson | Craig Sherstan
[1] Harry E. Stephanou,et al. A Hierarchical Approach to the Control of a Prosthetic Arm , 1977, IEEE Transactions on Systems, Man, and Cybernetics.
[2] V. Mathiowetz,et al. Adult norms for the Box and Block Test of manual dexterity. , 1985, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.
[3] Long Ji Lin,et al. Programming Robots Using Reinforcement Learning and Teaching , 1991, AAAI.
[4] L.-J. Lin,et al. Hierarchical learning of robot skills by reinforcement , 1993, IEEE International Conference on Neural Networks.
[5] Pat Langley,et al. Machine Learning for Adaptive User Interfaces , 1997, KI.
[6] C. Dewdney. Last Flesh: Life in the Transhuman Era , 1998 .
[7] M. Gazzaniga. Cerebral specialization and interhemispheric communication: does the corpus callosum enable the human condition? , 2000, Brain : a journal of neurology.
[8] Rodney A. Brooks,et al. Flesh and Machines: How Robots Will Change Us , 2002 .
[9] C. Light,et al. Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity. , 2002, Archives of physical medicine and rehabilitation.
[10] J. Geary. Body Electric: An Anatomy of the New Bionic Senses , 2002 .
[11] Pierre-Yves Oudeyer,et al. Robotic clicker training , 2002, Robotics Auton. Syst..
[12] K. Doya,et al. A unifying computational framework for motor control and social interaction. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[13] P. Bach-y-Rita,et al. Sensory substitution and the human–machine interface , 2003, Trends in Cognitive Sciences.
[14] Gul A. Agha,et al. Towards a hierarchical taxonomy of autonomous agents , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).
[15] Longxin Lin. Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching , 2004, Machine Learning.
[16] D. Feil-Seifer,et al. Defining socially assistive robotics , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..
[17] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[18] B Hudgins,et al. Myoelectric signal processing for control of powered limb prostheses. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[19] H. Bekkering,et al. Joint action: bodies and minds moving together , 2006, Trends in Cognitive Sciences.
[20] Jon A. Mukand,et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.
[21] J. Pincus. The Brain That Changes Itself: Stories of Personal Triumph From the Frontiers of Brain Science , 2008 .
[22] Kathryn Ziegler-Graham,et al. Estimating the prevalence of limb loss in the United States: 2005 to 2050. , 2008, Archives of physical medicine and rehabilitation.
[23] Andrea Lockerd Thomaz,et al. Teachable robots: Understanding human teaching behavior to build more effective robot learners , 2008, Artif. Intell..
[24] Peter Stone,et al. Interactively shaping agents via human reinforcement: the TAMER framework , 2009, K-CAP '09.
[25] Jacqueline S. Hebert,et al. Outcome measures in amputation rehabilitation: ICF body functions , 2009, Disability and rehabilitation.
[26] Natalie Sebanz,et al. Prediction in Joint Action: What, When, and Where , 2009, Top. Cogn. Sci..
[27] C. Neuper,et al. Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges , 2010, Front. Neurosci..
[28] S Micera,et al. Control of Hand Prostheses Using Peripheral Information , 2010, IEEE Reviews in Biomedical Engineering.
[29] Stefanie Tellex,et al. Toward understanding natural language directions , 2010, HRI 2010.
[30] Farbod Fahimi,et al. Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.
[31] Linda Resnik,et al. Development and testing of new upper-limb prosthetic devices: research designs for usability testing. , 2011, Journal of rehabilitation research and development.
[32] Patrick M. Pilarski,et al. Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction , 2011, AAMAS.
[33] M. Lundberg,et al. My prosthesis as a part of me: a qualitative analysis of living with an osseointegrated prosthetic limb , 2011, Prosthetics and orthotics international.
[34] T Walley Williams,et al. Progress on stabilizing and controlling powered upper-limb prostheses. , 2011, Journal of rehabilitation research and development.
[35] Giovanni Pezzulo,et al. What should I do next? Using shared representations to solve interaction problems , 2011, Experimental Brain Research.
[36] Frank Moss. The Sorcerers and Their Apprentices: How the Digital Magicians of the MIT Media Lab Are Creating the Innovative Technologies That Will Transform Our Lives , 2011 .
[37] Stefano Stramigioli,et al. Myoelectric forearm prostheses: state of the art from a user-centered perspective. , 2011, Journal of rehabilitation research and development.
[38] Estela Bicho,et al. Neuro-cognitive mechanisms of decision making in joint action: a human-robot interaction study. , 2011, Human movement science.
[39] Erik Scheme,et al. Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. , 2011, Journal of rehabilitation research and development.
[40] Peter Stone,et al. Reinforcement learning from simultaneous human and MDP reward , 2012, AAMAS.
[41] Patrick M. Pilarski,et al. Between Instruction and Reward: Human-Prompted Switching , 2012, AAAI Fall Symposium: Robots Learning Interactively from Human Teachers.
[42] L. Resnik,et al. Advanced upper limb prosthetic devices: implications for upper limb prosthetic rehabilitation. , 2012, Archives of physical medicine and rehabilitation.
[43] J. M. Carmena. Becoming Bionic , 2012, IEEE Spectrum.
[44] TaeChoong Chung,et al. Learning via human feedback in continuous state and action spaces , 2013, Applied Intelligence.
[45] Cynthia Breazeal,et al. Training a Robot via Human Feedback: A Case Study , 2013, ICSR.
[46] Patrick M. Pilarski,et al. Adaptive artificial limbs: a real-time approach to prediction and anticipation , 2013, IEEE Robotics & Automation Magazine.
[47] Alex Mihailidis,et al. A Survey on Ambient-Assisted Living Tools for Older Adults , 2013, IEEE Journal of Biomedical and Health Informatics.
[48] G. Pezzulo,et al. Human Sensorimotor Communication: A Theory of Signaling in Online Social Interactions , 2013, PloS one.
[49] Patrick M. Pilarski,et al. Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).
[50] Elizabeth A. Croft,et al. Design and impact of hesitation gestures during human-robot resource conflicts , 2013, HRI 2013.
[51] Andrea Lockerd Thomaz,et al. Policy Shaping: Integrating Human Feedback with Reinforcement Learning , 2013, NIPS.
[52] Jian Huang,et al. Study of reinforcement learning based shared control of walking-aid robot , 2013, Proceedings of the 2013 IEEE/SICE International Symposium on System Integration.
[53] David L. Roberts,et al. A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback , 2014, AAAI.
[54] Alan K. Mackworth,et al. A Wizard-of-Oz Intelligent Wheelchair Study with Cognitively-Impaired Older Adults : Attitudes toward User Control , 2014 .
[55] Patrick M. Pilarski,et al. Using Learned Predictions as Feedback to Improve Control and Communication with an Artificial Limb: Preliminary Findings , 2014, ArXiv.
[56] Panagiotis K. Artemiadis,et al. Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography , 2014, Front. Neurorobot..
[57] Dario Farina,et al. Stereovision and augmented reality for closed-loop control of grasping in hand prostheses , 2014, Journal of neural engineering.
[58] Maya Cakmak,et al. Power to the People: The Role of Humans in Interactive Machine Learning , 2014, AI Mag..
[59] J. Olson,et al. The evolution of functional hand replacement: From iron prostheses to hand transplantation. , 2014, Plastic surgery.
[60] J. Hebert,et al. Updates in Targeted Sensory Reinnervation for Upper Limb Amputation , 2014, Current Surgery Reports.
[61] Jacqueline S. Hebert,et al. Applications of sensory feedback in motorized upper extremity prosthesis: a review , 2014, Expert review of medical devices.
[62] Max Ortiz-Catalan,et al. An osseointegrated human-machine gateway for long-term sensory feedback and motor control of artificial limbs , 2014, Science Translational Medicine.
[63] Andrea Lockerd Thomaz,et al. Robot Learning from Human Teachers , 2014, Robot Learning from Human Teachers.
[64] Patrick M. Pilarski,et al. A Collaborative Approach to the Simultaneous Multi-joint Control of a Prosthetic Arm , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).
[65] A. Clark. Embodied Prediction , 2015 .
[66] P. Pilarski. Prosthetic Devices as Goal-Seeking Agents , 2015 .
[67] John Markoff,et al. Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots , 2015 .
[68] G. Pezzulo,et al. Interactional leader–follower sensorimotor communication strategies during repetitive joint actions , 2015, Journal of The Royal Society Interface.
[69] Peter Stone,et al. Framing reinforcement learning from human reward: Reward positivity, temporal discounting, episodicity, and performance , 2015, Artif. Intell..
[70] Patrick M. Pilarski,et al. Machine learning and unlearning to autonomously switch between the functions of a myoelectric arm , 2016, 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).
[71] Patrick M. Pilarski,et al. Face valuing: Training user interfaces with facial expressions and reinforcement learning , 2016, ArXiv.
[72] Chang Liu,et al. Cooperative Search Using Human-UAV Teams , 2016 .
[73] Patrick M. Pilarski,et al. Steps toward knowledgeable neuroprostheses , 2016, 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).
[74] Brian Scassellati,et al. Socially Assistive Robotics: Methods and Implications for the Future of Work and Care , 2022, Robophilosophy.
[75] Ann L. Edwards,et al. Adaptive and Autonomous Switching: Shared Control of Powered Prosthetic Arms Using Reinforcement Learning , 2016 .
[76] Patrick M. Pilarski,et al. Introspective Agents: Confidence Measures for General Value Functions , 2016, AGI.
[77] Patrick M. Pilarski,et al. Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning , 2016, ArXiv.