Recent Advances in Robot Learning from Demonstration

In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot ...

[1]  Patrick MacAlpine,et al.  Humanoid robots learning to walk faster: from the real world to simulation and back , 2013, AAMAS.

[2]  Alexey Dosovitskiy,et al.  End-to-End Driving Via Conditional Imitation Learning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Andrea Lockerd Thomaz,et al.  Learning from Corrective Demonstrations , 2019, 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[4]  Ken Goldberg,et al.  Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation , 2017, ICRA.

[5]  Athanasios S. Polydoros,et al.  Human-Machine Interface for Remote Training of Robot Tasks. , 2018, 2018 IEEE International Conference on Imaging Systems and Techniques (IST).

[6]  Dean Pomerleau,et al.  Efficient Training of Artificial Neural Networks for Autonomous Navigation , 1991, Neural Computation.

[7]  Marc Toussaint,et al.  Direct Loss Minimization Inverse Optimal Control , 2015, Robotics: Science and Systems.

[8]  Pieter Abbeel,et al.  Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion , 2007, NIPS.

[9]  Sandra Hirche,et al.  Learning Stable Stochastic Nonlinear Dynamical Systems , 2017, ICML.

[10]  Sonia Chernova,et al.  Construction of an object manipulation database from grasp demonstrations , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Maya Cakmak,et al.  Power to the People: The Role of Humans in Interactive Machine Learning , 2014, AI Mag..

[12]  Aude Billard,et al.  Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models , 2011, IEEE Transactions on Robotics.

[13]  Rüdiger Dillmann,et al.  Incremental Learning of Tasks From User Demonstrations, Past Experiences, and Vocal Comments , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Brijen Thananjeyan,et al.  SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards , 2018, Int. J. Robotics Res..

[15]  Kyungjae Lee,et al.  Robust learning from demonstration using leveraged Gaussian processes and sparse-constrained optimization , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Martin V. Butz,et al.  Learning to Reproduce Visually Similar Movements by Minimizing Event-Based Prediction Error , 2018, 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob).

[17]  Jan Peters,et al.  Learning to sequence movement primitives from demonstrations , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  S. Ghirlanda,et al.  A century of generalization , 2003, Animal Behaviour.

[19]  Gaurav S. Sukhatme,et al.  Learning Manipulation Graphs from Demonstrations Using Multimodal Sensory Signals , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Ana Paiva,et al.  An ensemble inverse optimal control approach for robotic task learning and adaptation , 2019, Auton. Robots.

[21]  Siddhartha S. Srinivasa,et al.  CHOMP: Covariant Hamiltonian optimization for motion planning , 2013, Int. J. Robotics Res..

[22]  Andrea Lockerd Thomaz,et al.  Human-Driven Feature Selection for a Robotic Agent Learning Classification Tasks from Demonstration , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[23]  Stefan Schaal,et al.  Learning, planning, and control for quadruped locomotion over challenging terrain , 2011, Int. J. Robotics Res..

[24]  Stefan Schaal,et al.  STOMP: Stochastic trajectory optimization for motion planning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[25]  J. Andrew Bagnell,et al.  Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy , 2010 .

[26]  Andrea Lockerd Thomaz,et al.  Asking for Help Effectively via Modeling of Human Beliefs , 2018, HRI.

[27]  Pieter Abbeel,et al.  An Algorithmic Perspective on Imitation Learning , 2018, Found. Trends Robotics.

[28]  H. Jin Kim,et al.  Planning and Control for Collision-Free Cooperative Aerial Transportation , 2018, IEEE Transactions on Automation Science and Engineering.

[29]  Jan Peters,et al.  Reinforcement learning in robotics: A survey , 2013, Int. J. Robotics Res..

[30]  Anca D. Dragan,et al.  Learning Robot Objectives from Physical Human Interaction , 2017, CoRL.

[31]  Yiannis Demiris,et al.  A nonparametric Bayesian approach toward robot learning by demonstration , 2012, Robotics Auton. Syst..

[32]  Ashutosh Saxena,et al.  Robobarista: Object Part Based Transfer of Manipulation Trajectories from Crowd-Sourcing in 3D Pointclouds , 2015, ISRR.

[33]  Vladlen Koltun,et al.  Deep Drone Racing: Learning Agile Flight in Dynamic Environments , 2018, CoRL.

[34]  Sylvain Calinon,et al.  Supervisory teleoperation with online learning and optimal control , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[35]  Markus Schneider,et al.  Robot Learning by Demonstration with local Gaussian process regression , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[36]  Jochen J. Steil,et al.  Learning movement primitives for force interaction tasks , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[37]  Jan Peters,et al.  Relative Entropy Inverse Reinforcement Learning , 2011, AISTATS.

[38]  Sanjay Krishnan,et al.  HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards , 2016, ArXiv.

[39]  Martial Hebert,et al.  Learning monocular reactive UAV control in cluttered natural environments , 2012, 2013 IEEE International Conference on Robotics and Automation.

[40]  Emanuel Todorov,et al.  Inverse Optimal Control with Linearly-Solvable MDPs , 2010, ICML.

[41]  Jan Peters,et al.  Bayesian Gait Optimization for Bipedal Locomotion , 2014, LION.

[42]  Dieter Fox,et al.  SE3-nets: Learning rigid body motion using deep neural networks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[43]  David Silver,et al.  Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain , 2010, Int. J. Robotics Res..

[44]  Scott Niekum,et al.  Learning and generalization of complex tasks from unstructured demonstrations , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[45]  Pieter Abbeel,et al.  Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization , 2013, Robotics: Science and Systems.

[46]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[47]  Pieter Abbeel,et al.  Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations , 2010, 2010 IEEE International Conference on Robotics and Automation.

[48]  Jan Peters,et al.  Probabilistic segmentation applied to an assembly task , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[49]  Andrea Lockerd Thomaz,et al.  Visual Case Retrieval for Interpreting Skill Demonstrations , 2015, ICCBR.

[50]  Stefano Caselli,et al.  Part-based robot grasp planning from human demonstration , 2011, 2011 IEEE International Conference on Robotics and Automation.

[51]  Markus Schneider,et al.  LAT: A simple Learning from Demonstration method , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[52]  Andrej Gams,et al.  Accelerated Sensorimotor Learning of Compliant Movement Primitives , 2018, IEEE Transactions on Robotics.

[53]  Maya Cakmak,et al.  Designing robot learners that ask good questions , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[54]  Dana Kulic,et al.  Incremental learning of full body motion primitives and their sequencing through human motion observation , 2012, Int. J. Robotics Res..

[55]  Ashwin P. Dani,et al.  Learning Partially Contracting Dynamical Systems from Demonstrations , 2017, CoRL.

[56]  Andrew Y. Ng,et al.  Pharmacokinetics of a novel formulation of ivermectin after administration to goats , 2000, ICML.

[57]  Stefan Schaal,et al.  Skill learning and task outcome prediction for manipulation , 2011, 2011 IEEE International Conference on Robotics and Automation.

[58]  Sylvain Calinon,et al.  Learning from demonstration for semi-autonomous teleoperation , 2019, Auton. Robots.

[59]  Sergey Levine,et al.  Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[60]  Oliver Kroemer,et al.  Interaction primitives for human-robot cooperation tasks , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[61]  Sonia Chernova,et al.  Interactive Hierarchical Task Learning from a Single Demonstration , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[62]  Sergey Levine,et al.  Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization , 2016, ICML.

[63]  Affan Pervez,et al.  Learning deep movement primitives using convolutional neural networks , 2017, 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids).

[64]  Carme Torras,et al.  Learning Collaborative Impedance-Based Robot Behaviors , 2013, AAAI.

[65]  Adrian Corduneanu,et al.  Variational Bayesian Model Selection for Mixture Distributions , 2001 .

[66]  Aaron R. Seitz,et al.  Benefits of multisensory learning , 2008, Trends in Cognitive Sciences.

[67]  Sonia Chernova,et al.  Construction of a 3D object recognition and manipulation database from grasp demonstrations , 2016, Auton. Robots.

[68]  Stefan Schaal,et al.  Movement segmentation using a primitive library , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[69]  Jan Peters,et al.  Policy Search for Motor Primitives in Robotics , 2008, NIPS 2008.

[70]  Tobias Doernbach,et al.  Dexterous Underwater Manipulation from Onshore Locations: Streamlining Efficiencies for Remotely Operated Underwater Vehicles , 2018, IEEE Robotics & Automation Magazine.

[71]  Richard Alan Peters,et al.  Robonaut task learning through teleoperation , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[72]  Brian Scassellati,et al.  Discovering task constraints through observation and active learning , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[73]  Siddhartha S. Srinivasa,et al.  Towards Robotic Feeding: Role of Haptics in Fork-Based Food Manipulation , 2018, IEEE Robotics and Automation Letters.

[74]  Pieter Abbeel,et al.  Apprenticeship learning via inverse reinforcement learning , 2004, ICML.

[75]  John R. Anderson,et al.  Interactive Task Learning , 2017, IEEE Intelligent Systems.

[76]  Stefan Schaal,et al.  Robot Programming by Demonstration , 2009, Springer Handbook of Robotics.

[77]  Affan Pervez,et al.  Learning task-parameterized dynamic movement primitives using mixture of GMMs , 2018, Intell. Serv. Robotics.

[78]  Jun Morimoto,et al.  Learning from demonstration and adaptation of biped locomotion , 2004, Robotics Auton. Syst..

[79]  Jun Nakanishi,et al.  Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors , 2013, Neural Computation.

[80]  Francesca Cordella,et al.  Learning by Demonstration for Planning Activities of Daily Living in Rehabilitation and Assistive Robotics , 2017, IEEE Robotics and Automation Letters.

[81]  Rüdiger Dillmann,et al.  What Can Robots Learn from Humans , 1996 .

[82]  Yoshihiko Nakamura,et al.  Statistical mutual conversion between whole body motion primitives and linguistic sentences for human motions , 2015, Int. J. Robotics Res..

[83]  Maya Cakmak,et al.  Keyframe-based Learning from Demonstration , 2012, Int. J. Soc. Robotics.

[84]  Martin A. Riedmiller,et al.  Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards , 2017, ArXiv.

[85]  Geoffrey A. Hollinger,et al.  Human–robot planning and learning for marine data collection , 2016, Auton. Robots.

[86]  Stefan Schaal,et al.  Towards Associative Skill Memories , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[87]  Andrea Lockerd Thomaz,et al.  Robot Learning from Human Teachers , 2014, Robot Learning from Human Teachers.

[88]  Kristian Kersting,et al.  Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[89]  Odest Chadwicke Jenkins,et al.  Learning from demonstration using a multi-valued function regressor for time-series data , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[90]  Gaurav S. Sukhatme,et al.  Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets , 2017, NIPS.

[91]  Darwin G. Caldwell,et al.  Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[92]  Byron Boots,et al.  Towards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning , 2017, CoRL.

[93]  Marc Carreras,et al.  An Intervention-AUV learns how to perform an underwater valve turning , 2014, OCEANS 2014 - TAIPEI.

[94]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[95]  Klaus Neumann,et al.  Learning robot motions with stable dynamical systems under diffeomorphic transformations , 2015, Robotics Auton. Syst..

[96]  Byron Boots,et al.  Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction , 2017, ICML.

[97]  Sonia Chernova,et al.  Integrating reinforcement learning with human demonstrations of varying ability , 2011, AAMAS.

[98]  Gaurav S. Sukhatme,et al.  Learning to Switch Between Sensorimotor Primitives Using Multimodal Haptic Signals , 2016, SAB.

[99]  Anca D. Dragan,et al.  SHIV: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[100]  Jun Morimoto,et al.  Motion capture and reinforcement learning of dynamically stable humanoid movement primitives , 2013, 2013 IEEE International Conference on Robotics and Automation.

[101]  Sonia Chernova,et al.  Skill Acquisition via Automated Multi-Coordinate Cost Balancing , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[102]  Paul Evrard,et al.  Learning collaborative manipulation tasks by demonstration using a haptic interface , 2009, ICAR.

[103]  Aude Billard,et al.  Learning object-level impedance control for robust grasping and dexterous manipulation , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[104]  H. Harry Asada,et al.  Direct teaching and automatic program generation for the hybrid control of robot manipulators , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[105]  Scott Niekum,et al.  Toward Probabilistic Safety Bounds for Robot Learning from Demonstration , 2017, AAAI Fall Symposia.

[106]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[107]  Manuel Lopes,et al.  Temporal segmentation of pair-wise interaction phases in sequential manipulation demonstrations , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[108]  Alex Mihailidis,et al.  Learning and Personalizing Socially Assistive Robot Behaviors to Aid with Activities of Daily Living , 2018, ACM Transactions on Human-Robot Interaction.

[109]  Siddhartha S. Srinivasa,et al.  Toward seamless human-robot handovers , 2013, Journal of Human-Robot Interaction.

[110]  Sergey Levine,et al.  Learning dexterous manipulation for a soft robotic hand from human demonstrations , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[111]  Christopher G. Atkeson,et al.  Optimization and learning for rough terrain legged locomotion , 2011, Int. J. Robotics Res..

[112]  Oliver Kroemer,et al.  Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks , 2017, Auton. Robots.

[113]  Eric L. Sauser,et al.  An Approach Based on Hidden Markov Model and Gaussian Mixture Regression , 2010 .

[114]  David Silver,et al.  Active learning from demonstration for robust autonomous navigation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[115]  Darwin G. Caldwell,et al.  Learning bimanual end-effector poses from demonstrations using task-parameterized dynamical systems , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[116]  David Whitney,et al.  Comparing Robot Grasping Teleoperation Across Desktop and Virtual Reality with ROS Reality , 2017, ISRR.

[117]  Shijian Li,et al.  Inverse Reinforcement Learning with Multiple Ranked Experts , 2019, ArXiv.

[118]  Oliver Kroemer,et al.  Learning to predict phases of manipulation tasks as hidden states , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[119]  Rouhollah Rahmatizadeh,et al.  Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[120]  Oliver Kroemer,et al.  Towards learning hierarchical skills for multi-phase manipulation tasks , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[121]  Danica Kragic,et al.  Robot Learning from Demonstration: A Task-level Planning Approach , 2008 .

[122]  Sergey Levine,et al.  Learning force-based manipulation of deformable objects from multiple demonstrations , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[123]  Dmitry Berenson,et al.  Simultaneous learning of hierarchy and primitives for complex robot tasks , 2019, Auton. Robots.

[124]  Yu Sun,et al.  Learning grasping force from demonstration , 2012, 2012 IEEE International Conference on Robotics and Automation.

[125]  Chu Vivian,et al.  Learning object affordances by leveraging the combination of human-guidance and self-exploration , 2016 .

[126]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[127]  Stefano Ermon,et al.  Generative Adversarial Imitation Learning , 2016, NIPS.

[128]  Ashwin P. Dani,et al.  Human Intention-Driven Learning Control for Trajectory Synchronization in Human-Robot Collaborative Tasks , 2019, IFAC-PapersOnLine.

[129]  Daniel H. Grollman,et al.  Incremental learning of subtasks from unsegmented demonstration , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[130]  Heni Ben Amor,et al.  A system for learning continuous human-robot interactions from human-human demonstrations , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[131]  Alexander Herzog,et al.  Learning of grasp selection based on shape-templates , 2014, Auton. Robots.

[132]  Stefan Schaal,et al.  Learning objective functions for manipulation , 2013, 2013 IEEE International Conference on Robotics and Automation.

[133]  Rüdiger Dillmann,et al.  Programming by demonstration of probabilistic decision making on a multi-modal service robot , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[134]  Byron Boots,et al.  Agile Autonomous Driving using End-to-End Deep Imitation Learning , 2017, Robotics: Science and Systems.

[135]  Brian Scassellati,et al.  Autonomously constructing hierarchical task networks for planning and human-robot collaboration , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[136]  Geoffrey J. Gordon,et al.  A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning , 2010, AISTATS.

[137]  Stefanie Tellex,et al.  Testing Robot Teleoperation using a Virtual Reality Interface with ROS Reality , 2018 .

[138]  Huosheng Hu,et al.  Robot Learning from Demonstration in Robotic Assembly: A Survey , 2018, Robotics.

[139]  Siddhartha S. Srinivasa,et al.  Movement primitives via optimization , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[140]  Stefanie Tellex,et al.  Learning from Crowdsourced Virtual Reality Demonstrations , 2018 .

[141]  Pieter Abbeel,et al.  Learning for control from multiple demonstrations , 2008, ICML '08.

[142]  Darwin G. Caldwell,et al.  Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input , 2011, Adv. Robotics.

[143]  Tadej Petric,et al.  Robotic assembly solution by human-in-the-loop teaching method based on real-time stiffness modulation , 2018, Auton. Robots.

[144]  Siddhartha S. Srinivasa,et al.  CHOMP: Gradient optimization techniques for efficient motion planning , 2009, 2009 IEEE International Conference on Robotics and Automation.

[145]  Athanasios S. Polydoros,et al.  Online multi-target learning of inverse dynamics models for computed-torque control of compliant manipulators , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[146]  Monica N. Nicolescu,et al.  Natural methods for robot task learning: instructive demonstrations, generalization and practice , 2003, AAMAS '03.

[147]  Silvio Savarese,et al.  ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation , 2018, CoRL.

[148]  Wolfram Burgard,et al.  Learning driving styles for autonomous vehicles from demonstration , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[149]  Mahdi Tavakoli,et al.  Kinesthetic teaching of a therapist's behavior to a rehabilitation robot , 2018, 2018 International Symposium on Medical Robotics (ISMR).

[150]  Peter Englert,et al.  Probabilistic model-based imitation learning , 2013, Adapt. Behav..

[151]  Manuela M. Veloso,et al.  Biped Walk Learning Through Playback and Corrective Demonstration , 2010, AAAI.

[152]  Andrej Gams,et al.  Learning Compliant Movement Primitives Through Demonstration and Statistical Generalization , 2016, IEEE/ASME Transactions on Mechatronics.

[153]  Joelle Pineau,et al.  Learning from Limited Demonstrations , 2013, NIPS.

[154]  Kee-Eung Kim,et al.  MAP Inference for Bayesian Inverse Reinforcement Learning , 2011, NIPS.

[155]  Sergey Levine,et al.  Time-Contrastive Networks: Self-Supervised Learning from Video , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[156]  Stefan Schaal,et al.  Learning and generalization of motor skills by learning from demonstration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[157]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[158]  Peter K. Allen,et al.  Robot learning of everyday object manipulations via human demonstration , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[159]  Julie A. Shah,et al.  Human-Machine Collaborative Optimization via Apprenticeship Scheduling , 2018, J. Artif. Intell. Res..

[160]  Rüdiger Dillmann,et al.  Learning of Planning Models for Dexterous Manipulation Based on Human Demonstrations , 2012, International Journal of Social Robotics.

[161]  Sonia Chernova,et al.  Unsupervised learning of multi-hypothesized pick-and-place task templates via crowdsourcing , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[162]  Oliver Kroemer,et al.  Structured Apprenticeship Learning , 2012, ECML/PKDD.

[163]  Maya Cakmak,et al.  Learning generalizable surface cleaning actions from demonstration , 2017, 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[164]  Sergey Levine,et al.  One-Shot Visual Imitation Learning via Meta-Learning , 2017, CoRL.

[165]  Darwin G. Caldwell,et al.  Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints , 2017, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[166]  Maya Cakmak,et al.  Designing Interactions for Robot Active Learners , 2010, IEEE Transactions on Autonomous Mental Development.

[167]  Rüdiger Dillmann,et al.  Teaching and learning of robot tasks via observation of human performance , 2004, Robotics Auton. Syst..

[168]  Pinhas Ben-Tzvi,et al.  Hand Rehabilitation Learning System With an Exoskeleton Robotic Glove , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[169]  David Silver,et al.  Learning to search: Functional gradient techniques for imitation learning , 2009, Auton. Robots.

[170]  Ville Kyrki,et al.  Learning compliant assembly motions from demonstration , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[171]  Aude Billard,et al.  Incremental motion learning with locally modulated dynamical systems , 2015, Robotics Auton. Syst..

[172]  Justus H. Piater,et al.  Bottom-up learning of object categories, action effects and logical rules: From continuous manipulative exploration to symbolic planning , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[173]  Sergey Levine,et al.  Deep visual foresight for planning robot motion , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[174]  Carlos R. del-Blanco,et al.  DroNet: Learning to Fly by Driving , 2018, IEEE Robotics and Automation Letters.

[175]  K. Dautenhahn,et al.  The correspondence problem , 2002 .

[176]  Danica Kragic,et al.  Data-Driven Grasp Synthesis—A Survey , 2013, IEEE Transactions on Robotics.

[177]  David J. Ketchen,et al.  THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE , 1996 .

[178]  Darwin G. Caldwell,et al.  A task-parameterized probabilistic model with minimal intervention control , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[179]  Andrea Lockerd Thomaz,et al.  Enhancing Robot Learning with Human Social Cues , 2019, 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[180]  Oleg O. Sushkov,et al.  A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[181]  Jan Peters,et al.  Learning interaction for collaborative tasks with probabilistic movement primitives , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[182]  Mahdi Tavakoli,et al.  Robotic assistance for children with cerebral palsy based on learning from tele-cooperative demonstration , 2017, International Journal of Intelligent Robotics and Applications.

[183]  Carme Torras,et al.  Robot learning from demonstration of force-based tasks with multiple solution trajectories , 2011, 2011 15th International Conference on Advanced Robotics (ICAR).

[184]  Anca D. Dragan,et al.  Cooperative Inverse Reinforcement Learning , 2016, NIPS.

[185]  Aude Billard,et al.  Learning from Humans , 2016, Springer Handbook of Robotics, 2nd Ed..

[186]  Athanasios S. Polydoros,et al.  Survey of Model-Based Reinforcement Learning: Applications on Robotics , 2017, J. Intell. Robotic Syst..

[187]  Aude Billard,et al.  Learning Augmented Joint-Space Task-Oriented Dynamical Systems: A Linear Parameter Varying and Synergetic Control Approach , 2018, IEEE Robotics and Automation Letters.

[188]  Jürgen Schmidhuber,et al.  A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks , 2006 .

[189]  Aude Billard,et al.  Online learning of varying stiffness through physical human-robot interaction , 2012, 2012 IEEE International Conference on Robotics and Automation.

[190]  Sergey Levine,et al.  Continuous Inverse Optimal Control with Locally Optimal Examples , 2012, ICML.

[191]  Pieter Abbeel,et al.  Autonomous Helicopter Aerobatics through Apprenticeship Learning , 2010, Int. J. Robotics Res..

[192]  Marcin Andrychowicz,et al.  Overcoming Exploration in Reinforcement Learning with Demonstrations , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[193]  Andrea Lockerd Thomaz,et al.  Towards Intelligent Arbitration of Diverse Active Learning Queries , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[194]  Stefano Ermon,et al.  InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations , 2017, NIPS.

[195]  Oliver Brock,et al.  Learning state representations with robotic priors , 2015, Auton. Robots.

[196]  Scott Niekum,et al.  Learning grounded finite-state representations from unstructured demonstrations , 2015, Int. J. Robotics Res..

[197]  Ashwin P. Dani,et al.  Learning position and orientation dynamics from demonstrations via contraction analysis , 2019, Auton. Robots.

[198]  Andrea Lockerd Thomaz,et al.  Active Attention-Modified Policy Shaping: Socially Interactive Agents Track , 2019, AAMAS.

[199]  Jan Peters,et al.  Probabilistic Movement Primitives , 2013, NIPS.

[200]  Ahmadzadeh S. Reza,et al.  Trajectory learning from demonstration with canal surfaces: A parameter-free approach , 2016 .

[201]  Anca D. Dragan,et al.  Learning from Physical Human Corrections, One Feature at a Time , 2018, 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[202]  Kalesha Bullard,et al.  Active Learning within Constrained Environments through Imitation of an Expert Questioner , 2019, IJCAI.

[203]  Jan Peters,et al.  Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations , 2018, IEEE Robotics and Automation Letters.

[204]  Hongliang Ren,et al.  Motion Planning Based on Learning From Demonstration for Multiple-Segment Flexible Soft Robots Actuated by Electroactive Polymers , 2016, IEEE Robotics and Automation Letters.

[205]  Matei T. Ciocarlie,et al.  Dimensionality reduction for hand-independent dexterous robotic grasping , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[206]  Anca D. Dragan,et al.  On the Utility of Model Learning in HRI , 2019, 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[207]  Scott Kuindersma,et al.  Robot learning from demonstration by constructing skill trees , 2012, Int. J. Robotics Res..

[208]  J A Bagnell,et al.  An Invitation to Imitation , 2015 .

[209]  Mamoru Mitsuishi,et al.  Trajectory planning under different initial conditions for surgical task automation by learning from demonstration , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[210]  Weichao Zhou,et al.  Safety-Aware Apprenticeship Learning , 2018, CAV.

[211]  Nicolas Perrin,et al.  Fast diffeomorphic matching to learn globally asymptotically stable nonlinear dynamical systems , 2016, Syst. Control. Lett..

[212]  Pieter Abbeel,et al.  Learning from Demonstrations Through the Use of Non-rigid Registration , 2013, ISRR.