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This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks.
arXiv preprint arXiv:1904.05240
Xu J, Hou Z, Liu Z and Qiao H (2019) Compare contact model-based control and contact model-free learning: A survey of robotic peg-in-hole assembly strategies · 1904
Earlier work this paper cites.
Pap n 6-C3 : 462–467
PJ M and BDO A (1971) Nonlinear regulator theory and an inverse optimal control problem · 1971
Earlier work this paper cites.
Journal of Dynamic Systems, Measurement, and Control 103(2): 126–133
Raibert MH and Craig JJ (1981) Hybrid position/force control of manipulators · 1981
Earlier work this paper cites.
Journal of Dynamic Systems, Measurement, and Control 104(1): 65–77
Whitney DE (1982) Quasi-static assembly of compliantly supported rigid parts · 1982
Earlier work this paper cites.
Trans. of the ASME Journal of Dynamic System, Measurement, and Control 107: 1
Neville H (1985) Impedance control: An approach to manipulation: Part i-iii · 1985
Earlier work this paper cites.
The International Journal of Robotics Research 5(3): 53–71
Mason MT (1986) Mechanics and planning of manipulator pushing operations · 1986
Earlier work this paper cites.
IEEE Journal on Robotics and Automation 3(1): 43–53
Khatib O (1987) A unified approach for motion and force control of robot manipulators: The operational space formulation · 1987
Earlier work this paper cites.
IEEE Transactions on Robotics and Automation 5(2): 151–165
Cutkosky MR and Kao I (1989) Computing and controlling compliance of a robotic hand · 1989
Earlier work this paper cites.
The International Journal of Robotics Research 12(2): 122–137
Bicchi A and Siciliano B (1993) Closure properties of robotic manipulation · 1993
Earlier work this paper cites.
IEEE Transactions on Rehabilitation Engineering 6(1): 75–87
Krebs HI, Hogan N, Aisen ML and Volpe BT (1998) Robot-aided neurorehabilitation · 1998
Earlier work this paper cites.
The International Journal of Robotics Research 18(11): 1056–1063
Arimoto S (1999) Robotics research toward explication of everyday physics · 1999
Earlier work this paper cites.
Springer Science & Business Media
Siciliano B and Villani L (1999) Robot force control · 1999
Earlier work this paper cites.
In: Icml , volume 1. p. 2
Ng AY and Russell S (2000) Algorithms for inverse reinforcement learning · 2000
Earlier work this paper cites.
IEEE Transactions on Robotics and Automation 9(5): 624–637
Lawrence DA (2002) Stability and transparency in bilateral teleoperation · 2002
Earlier work this paper cites.
In: The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003. pp. 309–314
Aleotti J, Caselli S and Reggiani M (2003) Toward programming of assembly tasks by demonstration in virtual environments · 2003
Earlier work this paper cites.
In: Workshop on Bilateral Paradigms on Humans and Humanoids: IEEE International Conference on Intelligent Robots and Systems (IROS 2003) . pp. 1–21
Schaal S, Peters J, Nakanishi J and Ijspeert A (2003) Control, planning, learning, and imitation with dynamic movement primitives · 2003
Earlier work this paper cites.
Robotics and Autonomous Systems 47(2-3): 109–116
Dillmann R (2004) Teaching and learning of robot tasks via observation of human performance · 2004
Earlier work this paper cites.
arXiv preprint arXiv:2005.01643
Levine S, Kumar A, Tucker G and Fu J (2020) Offline reinforcement learning: Tutorial, review, and perspectives on open problems · 2005
Earlier work this paper cites.
In: 2018 IEEE international conference on Robotics and Biomimetics (ROBIO) . IEEE, pp. 1999–2006
Zachiotis GA, Andrikopoulos G, Gornez R, Nakamura K and Nikolakopoulos G (2018) A survey on the application trends of home service robotics · 2006
Earlier work this paper cites.
Robotics and Autonomous Systems 57(5): 469–483
Argall BD, Chernova S, Veloso M and Browning B (2009) A survey of robot learning from demonstration · 2009
Earlier work this paper cites.
IEEE Robotics & Automation Magazine 17(2): 44–54
Calinon S, D’halluin F, Sauser EL, Caldwell DG and Billard AG (2010) Learning and reproduction of gestures by imitation · 2010
Earlier work this paper cites.
IEEE Robotics & Automation Magazine 17(2): 55–62
Kober J and Peters J (2010) Imitation and reinforcement learning · 2010
Earlier work this paper cites.
European Urology 57(2): 196–201
Nix J, Smith A, Kurpad R, Nielsen ME, Wallen EM and Pruthi RS (2010) Prospective randomized controlled trial of robotic versus open radical cystectomy for bladder cancer: perioperative and pathologic results · 2010
Earlier work this paper cites.
In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems . pp. 1837–1842
Osa T, Staub C and Knoll A (2010) Framework of automatic robot surgery system using visual servoing · 2010
Earlier work this paper cites.
In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics . JMLR Workshop and Conference Proceedings, pp. 661–668
Ross S and Bagnell D (2010) Efficient reductions for imitation learning · 2010
Earlier work this paper cites.
Neuron 72(3): 425–42
Franklin DW and Wolpert DM (2011) Computational mechanisms of sensorimotor control · 2011
Earlier work this paper cites.
IEEE Transactions on Robotics 27(5): 943–957
Khansari-Zadeh SM and Billard A (2011) Learning stable nonlinear dynamical systems with gaussian mixture models · 2011
Earlier work this paper cites.
Advanced Robotics 25(5): 581–603
Kormushev P, Calinon S and Caldwell DG (2011) Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input · 2011
Earlier work this paper cites.
The Lancet 377(9778): 1693–1702
Langhorne P, Bernhardt J and Kwakkel G (2011) Stroke rehabilitation · 2011
Earlier work this paper cites.
In: 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012) . IEEE, pp. 204–209
Büscher G, Koiva R, Schürmann C, Haschke R and Ritter HJ (2012) Tactile dataglove with fabric-based sensors · 2012
Earlier work this paper cites.
In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, pp. 5026–5033
Todorov E, Erez T and Tassa Y (2012) Mujoco: A physics engine for model-based control · 2012
Earlier work this paper cites.
The International Journal of Robotics Research 32(11): 1238–1274
Kober J, Bagnell JA and Peters J (2013) Reinforcement learning in robotics: A survey · 2013
Earlier work this paper cites.
Robotics and Autonomous Systems 61(12): 1323–1334
Lee K, Su Y, Kim TK and Demiris Y (2013) A syntactic approach to robot imitation learning using probabilistic activity grammars · 2013
Earlier work this paper cites.
In: 2013 16th International Conference on Advanced Robotics (ICAR) . IEEE, pp. 1–7
Nemec B, Abu-Dakka FJ, Ridge B, Ude A, Jørgensen JA, Savarimuthu TR, Jouffroy J, Petersen HG and Krüger N (2013) Transfer of assembly operations to new workpiece poses by adaptation to the desired force profile · 2013
Earlier work this paper cites.
Advances in Neural Information Processing Systems 26
Paraschos A, Daniel C, Peters JR and Neumann G (2013) Probabilistic movement primitives · 2013
Earlier work this paper cites.
Advances in Neural Information Processing Systems 27
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y (2014) Generative adversarial nets · 2014
Earlier work this paper cites.
Autonomous Robots 36(1-2): 123–136
Peternel L, Petrič T, Oztop E and Babič J (2014) Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach · 2014
Earlier work this paper cites.
In: 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 2997–3004
Ude A, Nemec B, Petrić T and Morimoto J (2014) Orientation in cartesian space dynamic movement primitives · 2014
Earlier work this paper cites.
IEEE/ASME Transactions on Mechatronics 21(5): 2581–2594
Deniša M, Gams A, Ude A and Petrič T (2015) Learning compliant movement primitives through demonstration and statistical generalization · 2015
Earlier work this paper cites.
In: Mensch und computer 2015–proceedings . De Gruyter Oldenbourg, pp. 223–232
Kittmann R, Fröhlich T, Schäfer J, Reiser U, Weißhardt F and Haug A (2015) Let me introduce myself: I am care-o-bot 4, a gentleman robot · 2015
Earlier work this paper cites.
In: 2015 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 3192–3199
Kober J, Gienger M and Steil JJ (2015) Learning movement primitives for force interaction tasks · 2015
Earlier work this paper cites.
In: 2015 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 1497–1502
Peternel L, Petrič T and Babič J (2015) Human-in-the-loop approach for teaching robot assembly tasks using impedance control interface · 2015
Earlier work this paper cites.
IEEE Transactions on Robotics 31(6): 1458–1471
Ureche ALP, Umezawa K, Nakamura Y and Billard A (2015) Task parameterization using continuous constraints extracted from human demonstrations · 2015
Earlier work this paper cites.
Parkinsonism & Related Disorders 22: S60–S64
Abbruzzese G, Marchese R, Avanzino L and Pelosin E (2016) Rehabilitation for parkinson’s disease: current outlook and future challenges · 2016
Earlier work this paper cites.
https://pybullet.org
Coumans E and Bai Y (2016) Pybullet, a python module for physics simulation for games, robotics and machine learning · 2016
Earlier work this paper cites.
In: Advances in Neural Information Processing Systems , volume 29
Ho J and Ermon S (2016) Generative adversarial imitation learning · 2016
Earlier work this paper cites.
Journal of Machine Learning Research 17(39): 1–40
Levine S, Finn C, Darrell T and Abbeel P (2016) End-to-end training of deep visuomotor policies · 2016
Earlier work this paper cites.
PLOS ONE 11(2): 1–26
Peternel L, Noda T, Petrič T, Ude A, Morimoto J and Babič J (2016) Adaptive control of exoskeleton robots for periodic assistive behaviours based on emg feedback minimisation · 2016
Earlier work this paper cites.
In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 3928–3934
Van Hoof H, Chen N, Karl M, Van Der Smagt P and Peters J (2016) Stable reinforcement learning with autoencoders for tactile and visual data · 2016
Earlier work this paper cites.
IEEE Robotics and Automation Letters 2(2): 397–403
Yang PC, Sasaki K, Suzuki K, Kase K, Sugano S and Ogata T (2016) Repeatable folding task by humanoid robot worker using deep learning · 2016
Earlier work this paper cites.
Advances in Neural Information Processing Systems 30
Duan Y, Andrychowicz M, Stadie B, Jonathan Ho O, Schneider J, Sutskever I, Abbeel P and Zaremba W (2017) One-shot imitation learning · 2017
Earlier work this paper cites.
In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 3530–3537
Edmonds M, Gao F, Xie X, Liu H, Qi S, Zhu Y, Rothrock B and Zhu SC (2017) Feeling the force: Integrating force and pose for fluent discovery through imitation learning to open medicine bottles · 2017
Earlier work this paper cites.
The International Journal of Robotics Research 37: 027836491774379
Englert P and Toussaint M (2017) Learning manipulation skills from a single demonstration · 2017
Earlier work this paper cites.
The International Journal of Robotics Research 36: 027836491774598
Englert P, Vien N and Toussaint M (2017) Inverse kkt: Learning cost functions of manipulation tasks from demonstrations · 2017
Earlier work this paper cites.
In: Conference on Robot Learning . pp. 357–368
Finn C, Yu T, Zhang T, Abbeel P and Levine S (2017) One-shot visual imitation learning via meta-learning · 2017
Earlier work this paper cites.
In: 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 2146–2153
Nair A, Chen D, Agrawal P, Isola P, Abbeel P, Malik J and Levine S (2017) Combining self-supervised learning and imitation for vision-based rope manipulation · 2017
Earlier work this paper cites.
IEEE Robotics and Automation Letters 2(2): 719–726
Peternel L, Rozo L, Caldwell D and Ajoudani A (2017) A method for derivation of robot task-frame control authority from repeated sensory observations · 2017
Earlier work this paper cites.
Frontiers in Computational Neuroscience 11(May): 1–12
Petrič T, Simpson CS, Ude A and Ijspeert AJ (2017) Hammering Does Not Fit Fitts’ Law · 2017
Earlier work this paper cites.
arXiv preprint arXiv:1709.10087
Rajeswaran A, Kumar V, Gupta A, Vezzani G, Schulman J, Todorov E and Levine S (2017) Learning complex dexterous manipulation with deep reinforcement learning and demonstrations · 2017
Earlier work this paper cites.
In: Proceedings of Robotics: Science and Systems
Sermanet P, Xu K and Levine S (2017) Unsupervised perceptual rewards for imitation learning · 2017
Earlier work this paper cites.
In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 23–30
Tobin J, Fong R, Ray A, Schneider J, Zaremba W and Abbeel P (2017) Domain randomization for transferring deep neural networks from simulation to the real world · 2017
Earlier work this paper cites.
In: NIPS
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A, Kaiser L and Polosukhin I (2017) Attention is all you need · 2017
Earlier work this paper cites.
arXiv preprint arXiv:1707.08817
Vecerik M, Hester T, Scholz J, Wang F, Pietquin O, Piot B, Heess N, Rothörl T, Lampe T and Riedmiller M (2017) Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards · 2017
Earlier work this paper cites.
In: 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 2882–2889
Vogt D, Stepputtis S, Grehl S, Jung B and Amor HB (2017) A system for learning continuous human-robot interactions from human-human demonstrations · 2017
Earlier work this paper cites.
Advances in Neural Information Processing Systems 30
Wang Z, Merel JS, Reed SE, de Freitas N, Wayne G and Heess N (2017) Robust imitation of diverse behaviors · 2017
Earlier work this paper cites.
IEEE Robotics and Automation Letters 2(3): 1240–1247
Zeestraten MJ, Havoutis I, Silvério J, Calinon S and Caldwell DG (2017) An approach for imitation learning on riemannian manifolds · 2017
Earlier work this paper cites.
In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 3648–3653
Adachi T, Fujimoto K, Sakaino S and Tsuji T (2018) Imitation learning for object manipulation based on position/force information using bilateral control · 2018
Earlier work this paper cites.
IEEE Robotics and Automation Letters 3(4): 3606–3613
Kutsuzawa K, Sakaino S and Tsuji T (2018) Sequence-to-sequence model for trajectory planning of nonprehensile manipulation including contact model · 2018
Earlier work this paper cites.
IEEE Transactions on Automation Science and Engineering 15(2): 675–691
Osa T, Sugita N and Mitsuishi M (2018) Online trajectory planning and force control for automation of surgical tasks · 2018
Earlier work this paper cites.
In: 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 3803–3810
Peng XB, Andrychowicz M, Zaremba W and Abbeel P (2018) Sim-to-real transfer of robotic control with dynamics randomization · 2018
Earlier work this paper cites.
IEEE Transactions on Robotics 34(6): 1636–1642
Petric T, Gams A, Colasanto L, Ijspeert AJ and Ude A (2018) Accelerated Sensorimotor Learning of Compliant Movement Primitives · 2018
Earlier work this paper cites.
In: International Conference on Learning Representations
Pong V, Gu S, Dalal M and Levine S (2018) Temporal difference models: Model-free deep rl for model-based control · 2018
Earlier work this paper cites.
In: 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 3758–3765
Rahmatizadeh R, Abolghasemi P, Bölöni L and Levine S (2018) Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration · 2018
Earlier work this paper cites.
IEEE Transactions on Neural Networks and Learning Systems 30(3): 777–787
Yang C, Chen C, He W, Cui R and Li Z (2018) Robot learning system based on adaptive neural control and dynamic movement primitives · 2018
Earlier work this paper cites.
In: Robotics: Science and Systems (RSS)
Yu T, Finn C, Xie A, Dasari S, Zhang T, Abbeel P and Levine S (2018) One-shot imitation from observing humans via domain-adaptive meta-learning · 2018
Earlier work this paper cites.
In: 2018 IEEE International Conference on Robotics and Automation (ICRA) . Ieee, pp. 5628–5635
Zhang T, McCarthy Z, Jow O, Lee D, Chen X, Goldberg K and Abbeel P (2018) Deep imitation learning for complex manipulation tasks from virtual reality teleoperation · 2018
Earlier work this paper cites.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . pp. 4254–4262
Abolghasemi P, Mazaheri A, Shah M and Boloni L (2019) Pay attention!-robustifying a deep visuomotor policy through task-focused visual attention · 2019
Earlier work this paper cites.
Robotics: Science and Systems XV
Campbell J, Stepputtis S and Ben Amor H (2019) Probabilistic multimodal modeling for human-robot interaction tasks · 2019
Earlier work this paper cites.
2019 International Conference on Robotics and Automation (ICRA) : 8973–8979
Chebotar Y, Handa A, Makoviychuk V, Macklin M, Issac J, Fox D and Molchanov A (2019) Closing the sim-to-real loop: Adapting simulation randomization with real world experience · 2019
Earlier work this paper cites.
Frontiers in Neurorobotics 13: 6
Degrave J, Hermans M, Dambre J and Wyffels F (2019) A differentiable physics engine for deep learning in robotics · 2019
Earlier work this paper cites.
In: 2019 International Conference on Robotics and Automation (ICRA) . IEEE, pp. 811–817
Fan Y, Luo J and Tomizuka M (2019) A learning framework for high precision industrial assembly · 2019
Earlier work this paper cites.
International Journal of Intelligent Robotics and Applications 3: 362–369
Fang B, Jia S, Guo D, Xu M, Wen S and Sun F (2019) Survey of imitation learning for robotic manipulation · 2019
Earlier work this paper cites.
The International Journal of Robotics Research 38(7): 833–852
Huang Y, Rozo L, Silvério J and Caldwell DG (2019) Kernelized movement primitives · 2019
Earlier work this paper cites.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . pp. 12627–12637
James S, Davison AJ and Johns E (2019) Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks · 2019
Earlier work this paper cites.
In: 2019 International Conference on Robotics and Automation (ICRA) . pp. 8077–8083
Kelly M, Sidrane C, Driggs-Campbell K and Kochenderfer MJ (2019) Hg-dagger: Interactive imitation learning with human experts · 2019
Earlier work this paper cites.
Science Robotics 4(26): eaav3150
Lázaro-Gredilla M, Lin D, Guntupalli JS and George D (2019) Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs · 2019
Earlier work this paper cites.
In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . pp. 10601–10610
Li Y, Zhu JY, Tedrake R and Torralba A (2019) Connecting touch and vision via cross-modal prediction · 2019
Earlier work this paper cites.
In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 1010–1017
Martín-Martín R, Lee MA, Gardner R, Savarese S, Bohg J and Garg A (2019) Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks · 2019
Earlier work this paper cites.
In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 4309–4316
Scherzinger S, Roennau A and Dillmann R (2019) Contact skill imitation learning for robot-independent assembly programming · 2019
Earlier work this paper cites.
In: Robotics: Science and Systems , volume 10
Seker MY, Imre M, Piater JH and Ugur E (2019) Conditional neural movement primitives · 2019
Cited alongside, same era.
In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (iros) . IEEE, pp. 8246–8251
Solak G and Jamone L (2019) Learning by demonstration and robust control of dexterous in-hand robotic manipulation skills · 2019
Cited alongside, same era.
https://drake.mit.edu
Tedrake R and the Drake Development Team (2019) Drake: A planning, control, and analysis toolbox for nonlinear dynamical systems · 2019
Cited alongside, same era.
In: 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) . IEEE, pp. 274–280
Tsurumine Y, Cui Y, Yamazaki K and Matsubara T (2019) Generative adversarial imitation learning with deep p-network for robotic cloth manipulation · 2019
Cited alongside, same era.
In: 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 4421–4426
Abu-Dakka FJ and Kyrki V (2020) Geometry-aware dynamic movement primitives · 2020
Cited alongside, same era.
The International Journal of Robotics Research : 02783649241281508
Firoozi R, Tucker J, Tian S, Majumdar A, Sun J, Liu W, Zhu Y, Song S, Kapoor A, Hausman K, Ichter B, Driess D, Wu J, Lu C and Schwager M (2023) Foundation models in robotics: Applications, challenges, and the future · 2023
Later among the works it cites.
IEEE Robotics and Automation Letters 8(10): 6083–6090
Hejrati M and Mattila J (2023) Nonlinear subsystem-based adaptive impedance control of physical human-robot-environment interaction in contact-rich tasks · 2023
Later among the works it cites.
IEEE Robotics and Automation Letters 8(12): 8271–8278
Ichiwara H, Ito H, Yamamoto K, Mori H and Ogata T (2023) Modality attention for prediction-based robot motion generation: Improving interpretability and robustness of using multi-modality · 2023
Later among the works it cites.
Nonlinear Dynamics 111(1): 113–127
Khandelwal A, Kant N and Mukherjee R (2023) Nonprehensile manipulation of a stick using impulsive forces · 2023
Later among the works it cites.
In: Proceedings of Robotics: Science and Systems
Kumar A, Singh A, Ebert FD, Nakamoto M, Yang Y, Finn C and Levine S (2023) Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Frontiers in Robotics and AI 7: 590681
Abu-Dakka FJ and Saveriano M (2020) Variable impedance control and learning—a review · 2020
Cited alongside, same era.
Advances in Neural Information Processing Systems 33: 5058–5069
Bahl S, Mukadam M, Gupta A and Pathak D (2020) Neural dynamic policies for end-to-end sensorimotor learning · 2020
Cited alongside, same era.
In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) . IEEE, pp. 4795–4798
Batson JP, Kato Y, Shuster K, Patton JL, Reed KB, Tsuji T and Novak D (2020) Haptic coupling in dyads improves motor learning in a simple force field · 2020
Cited alongside, same era.
Applied Sciences 10(19): 6923
Beltran-Hernandez CC, Petit D, Ramirez-Alpizar IG and Harada K (2020) Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approach · 2020
Cited alongside, same era.
IEEE Robotics and Automation Letters 5(2): 3533–3539
Bonardi A, James S and Davison AJ (2020) Learning one-shot imitation from humans without humans · 2020
Cited alongside, same era.
Applied Sciences 10(8): 2719
Cho NJ, Lee SH, Kim JB and Suh IH (2020) Learning, improving, and generalizing motor skills for the peg-in-hole tasks based on imitation learning and self-learning · 2020
Cited alongside, same era.
In: Conference on Robot Learning (CoRL)
Ferguson S, Liu S, Mandikal V, Liu K, Goldberg K and Thananjeyan B (2020) Leveraging demonstrations for reinforcement learning with hybrid representations · 2020
Cited alongside, same era.
IEEE Robotics and Automation Letters 8(4): 2325–2332
Li G, Jin Z, Volpp M, Otto F, Lioutikov R and Neumann G (2023) Prodmp: A unified perspective on dynamic and probabilistic movement primitives · 2023
Later among the works it cites.
Advanced Engineering Informatics 58: 102140
Li R and Zou Z (2023) Enhancing construction robot learning for collaborative and long-horizon tasks using generative adversarial imitation learning · 2023
Later among the works it cites.
In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) . IEEE, pp. 1–5
Lim JH, He K, Yi Z, Hou C, Zhang C, Sui Y and Li L (2023) Adaptive learning based upper-limb rehabilitation training system with collaborative robot · 2023
Later among the works it cites.
In: Proceedings of The 7th Conference on Robot Learning , volume 229. pp. 1348–1361
Luo J, Dong P, Wu J, Kumar A, Geng X and Levine S (2023) Action-quantized offline reinforcement learning for robotic skill learning · 2023
Later among the works it cites.
In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 8155–8162
Okada M, Komatsu M, Okumura R and Taniguchi T (2023) Learning compliant stiffness by impedance control-aware task segmentation and multi-objective bayesian optimization with priors · 2023
Later among the works it cites.
In: Proceedings of The 7th Conference on Robot Learning . pp. 3654–3671
Rafailov R, Hatch KB, Kolev V, Martin JD, Phielipp M and Finn C (2023) Moto: Offline pre-training to online fine-tuning for model-based robot learning · 2023
Later among the works it cites.
ACM Computing Surveys 55(10): 1–17
Rethmeier N and Augenstein I (2023) A primer on contrastive pretraining in language processing: Methods, lessons learned, and perspectives · 2023
Later among the works it cites.
In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . pp. 6469–6476
Royo-Miquel J, Hamaya M, Beltran-Hernandez CC and Tanaka K (2023) Learning robotic assembly by leveraging physical softness and tactile sensing · 2023
Later among the works it cites.
The International Journal of Robotics Research 42(13): 1133–1184
Saveriano M, Abu-Dakka FJ, Kramberger A and Peternel L (2023) Dynamic movement primitives in robotics: A tutorial survey · 2023
Later among the works it cites.
In: Conference on Robot Learning . PMLR, pp. 785–799
Shridhar M, Manuelli L and Fox D (2023) Perceiver-actor: A multi-task transformer for robotic manipulation · 2023
Later among the works it cites.
In: Conference on Robot Learning . PMLR, pp. 3397–3417
Stone A, Xiao T, Lu Y, Gopalakrishnan K, Lee KH, Vuong Q, Wohlhart P, Kirmani S, Zitkovich B, Xia F, Finn C and Hausman K (2023) Open-world object manipulation using pre-trained vision-language models · 2023
Later among the works it cites.
In: Proceedings of The 7th Conference on Robot Learning , volume 229. pp. 1723–1736
Walke HR, Black K, Zhao TZ, Vuong Q, Zheng C, Hansen-Estruch P, He AW, Myers V, Kim MJ, Du M, Lee A, Fang K, Finn C and Levine S (2023) Bridgedata v2: A dataset for robot learning at scale · 2023
Later among the works it cites.
International Journal of Robotics Research 2(2): 11–20
Wang A (2023) Efficient assembly in robotics · 2023
Later among the works it cites.
In: Proceedings of The 7th Conference on Robot Learning , volume 229. pp. 201–221
Wang C, Fan L, Sun J, Zhang R, Fei-Fei L, Xu D, Zhu Y and Anandkumar A (2023) Mimicplay: Long-horizon imitation learning by watching human play · 2023
Later among the works it cites.
In: Conference on Robot Learning . PMLR, pp. 2226–2240
Wu P, Escontrela A, Hafner D, Abbeel P and Goldberg K (2023) Daydreamer: World models for physical robot learning · 2023
Later among the works it cites.
In: Conference on Robot Learning . PMLR, pp. 2323–2339
Xian Z, Gkanatsios N, Gervet T, Ke TW and Fragkiadaki K (2023) Chaineddiffuser: Unifying trajectory diffusion and keypose prediction for robotic manipulation · 2023
Later among the works it cites.
In: Conference on Robot Learning . PMLR, pp. 1–10
Xiong H, Fu H, Zhang J, Bao C, Zhang Q, Huang Y, Xu W, Garg A and Lu C (2023) Robotube: Learning household manipulation from human videos with simulated twin environments · 2023
Later among the works it cites.
In: 2023 IEEE International Conference on Robotics and Automation (ICRA) . pp. 5829–5836
Yang W, Angleraud A, Pieters RS, Pajarinen J and Kämäräinen JK (2023) Seq2seq imitation learning for tactile feedback-based manipulation · 2023
Later among the works it cites.
The International Journal of Advanced Manufacturing Technology 125(9): 3981–4012
Zeng X, Zhu G, Gao Z, Ji R, Ansari J and Lu C (2023) Surface polishing by industrial robots: a review · 2023
Later among the works it cites.
Journal of Robotics 1(1): 1–10
Zhang A (2023) Learning adaptation for robotic operation · 2023
Later among the works it cites.
IEEE Robotics and Automation Letters 8(9): 5512–5519
Zhang F and Demiris Y (2023) Visual-tactile learning of garment unfolding for robot-assisted dressing · 2023
Later among the works it cites.
In: Proceedings of Robotics: Science and Systems
Zhang Y, Ke L, Deshpande A, Gupta A and Srinivasa S (2023) Cherry-Picking with Reinforcement Learning · 2023
Later among the works it cites.
In: Proceedings of Robotics: Science and Systems . Daegu, Republic of Korea
Zhao TZ, Kumar V, Levine S and Finn C (2023) Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware · 2023
Later among the works it cites.
In: 2023 IEEE International Conference on Robotics and Automation (ICRA) . pp. 7176–7183
Zhou G, Ke L, Srinivasa S, Gupta A, Rajeswaran A and Kumar V (2023) Real world offline reinforcement learning with realistic data source · 2023
Later among the works it cites.
In: Proceedings of The 7th Conference on Robot Learning , volume 229. pp. 2165–2183
Zitkovich B, Yu T, Xu S, Xu P, Xiao T, Xia F, Wu J, Wohlhart P, Welker S, Wahid A, Vuong Q, Vanhoucke V, Tran H, Soricut R, Singh A, Singh J, Sermanet P, Sanketi PR, Salazar G, Ryoo MS, Reymann K, Rao K, Pertsch K, Mordatch I, Michalewski H, Lu Y, Levine S, Lee L, Lee TWE, Leal I, Kuang Y, Kalashnikov D, Julian R, Joshi NJ, Irpan A, Ichter B, Hsu J, Herzog A, Hausman K, Gopalakrishnan K, Fu C, Florence P, Finn C, Dubey KA, Driess D, Ding T, Choromanski KM, Chen X, Chebotar Y, Carbajal J, Brown N, Brohan A, Arenas MG and Han K (2023) Rt-2: Vision-language-action models transfer web knowledge to robotic control · 2023
Later among the works it cites.
IEEE Transactions on Cognitive and Developmental Systems 15(4): 1812–1824
Özdemir O, Kerzel M, Weber C, Hee Lee J and Wermter S (2023) Language-model-based paired variational autoencoders for robotic language learning · 2023
Later among the works it cites.
IEEE Transactions on Robotics
Ablett T, Limoyo O, Sigal A, Jilani A, Kelly J, Siddiqi K, Hogan F and Dudek G (2024) Multimodal and force-matched imitation learning with a see-through visuotactile sensor · 2024
Later among the works it cites.
Neurocomputing 598: 128056
Abu-Dakka FJ, Saveriano M and Kyrki V (2024) A unified formulation of geometry-aware discrete dynamic movement primitives · 2024
Later among the works it cites.
In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 5096–5103
Ankile L, Simeonov A, Shenfeld I and Agrawal P (2024a) Juicer: Data-efficient imitation learning for robotic assembly · 2024
Later among the works it cites.
In: 2024 IEEE International Conference on Robotics and Automation (ICRA) . pp. 16977–16984
Bhateja C, Guo D, Ghosh D, Singh A, Tomar M, Vuong Q, Chebotar Y, Levine S and Kumar A (2024) Robotic offline rl from internet videos via value-function learning · 2024
Later among the works it cites.
Transactions on Machine Learning Research URL https://openreview.net/forum?id=vsCpILiWHu
Bousmalis K, Vezzani G, Rao D, Devin CM, Lee AX, Villalonga MB, Davchev T, Zhou Y, Gupta A, Raju A, Laurens A, Fantacci C, Dalibard V, Zambelli M, Martins MF, Pevceviciute R, Blokzijl M, Denil M, Batchelor N, Lampe T, Parisotto E, Zolna K, Reed S, Colmenarejo SG, Scholz J, Abdolmaleki A, Groth O, Regli JB, Sushkov O, Rothörl T, Chen JE, Aytar Y, Barker D, Ortiz J, Riedmiller M, Springenberg JT, Hadsell R, Nori F and Heess N (2024) Robocat: A self-improving generalist agent for robotic manipulation · 2024
Later among the works it cites.
In: 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) . pp. 410–415
Buamanee T, Kobayashi M, Uranishi Y and Takemura H (2024) Bi-act: Bilateral control-based imitation learning via action chunking with transformer · 2024
Later among the works it cites.
arXiv preprint arXiv:2411.18825
Chen L and Gombolay M (2024) Elemental: Interactive learning from demonstrations and vision-language models for reward design in robotics · 2024
Later among the works it cites.
URL https://arxiv.org/abs/2406.03813
Cheng N, Guan C, Gao J, Wang W, Li Y, Meng F, Zhou J, Fang B, Xu J and Han W (2024) Touch100k: A large-scale touch-language-vision dataset for touch-centric multimodal representation · 2024
Later among the works it cites.
In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . pp. 8531–8538
Deshpande A, Ke L, Pfeifer Q, Gupta A and Srinivasa SS (2024) Data efficient behavior cloning for fine manipulation via continuity-based corrective labels · 2024
Later among the works it cites.
In: 2024 IEEE International Conference on Robotics and Automation (ICRA) . pp. 5448–5454
Dong Q, Kaneko T and Sugiyama M (2024) An offline learning of behavior correction policy for vision-based robotic manipulation · 2024
Later among the works it cites.
In: 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 653–660
Fang HS, Fang H, Tang Z, Liu J, Wang C, Wang J, Zhu H and Lu C (2024) Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot · 2024
Later among the works it cites.
arXiv e-prints : arXiv–2403
George A, Gano S, Katragadda P and Barati Farimani A (2024) Visuo-tactile pretraining for cable plugging · 2024
Later among the works it cites.
In: Proceedings of Robotics: Science and Systems
Ghosh D, Walke HR, Pertsch K, Black K, Mees O, Dasari S, Hejna J, Kreiman T, Xu C, Luo J, Tan YL, Chen LY, Vuong Q, Xiao T, Sanketi PR, Sadigh D, Finn C and Levine S (2024) Octo: An Open-Source Generalist Robot Policy · 2024
Later among the works it cites.
8th Annual Conference on Robot Learning URL https://openreview.net/forum?id=xYJn2e1uu8
Higuera C, Sharma A, Bodduluri CK, Fan T, Lancaster P, Kalakrishnan M, Kaess M, Boots B, Lambeta M, Wu T and Mukadam M (2024) Sparsh: Self-supervised touch representations for vision-based tactile sensing · 2024
Later among the works it cites.
In: 8th Annual Conference on Robot Learning
Huang B, Wang Y, Yang X, Luo Y and Li Y (2024) 3d-vitac: Learning fine-grained manipulation with visuo-tactile sensing · 2024
Later among the works it cites.
In: 8th Annual Conference on Robot Learning
Jia X, Wang Q, Donat A, Xing B, Li G, Zhou H, Celik O, Blessing D, Lioutikov R and Neumann G (2024) MaIL: Improving imitation learning with selective state space models · 2024
Later among the works it cites.
IEEE Open Journal of Engineering in Medicine and Biology 5: 173–179
Kato Y, Tsuji T and Cikajlo I (2024) Feedback type may change the emg pattern and kinematics during robot supported upper limb reaching task · 2024
Later among the works it cites.
Advanced Robotics 38(18): 1232–1254
Kawaharazuka K, Matsushima T, Gambardella A, Guo J, Paxton C and Zeng A (2024) Real-world robot applications of foundation models: A review · 2024
Later among the works it cites.
In: Proceedings of Robotics: Science and Systems . Delft, Netherlands
Khazatsky A, Pertsch K, Nair S, Balakrishna A, Dasari S, Karamcheti S, Nasiriany S, Srirama MK, Chen LY, Ellis K, Fagan PD, Hejna J, Itkina M, Lepert M, Ma YJ, Miller PT, Wu J, Belkhale S, Dass S, Ha H, Jain A, Lee A, Lee Y, Memmel M, Park S, Radosavovic I, Wang K, Zhan A, Black K, Chi C, Hatch KB, Lin S, Lu J, Mercat J, Rehman A, Sanketi PR, Sharma A, Simpson C, Vuong Q, Walke HR, Wulfe B, Xiao T, Yang JH, Yavary A, Zhao TZ, Agia C, Baijal R, Castro MG, Chen D, Chen Q, Chung T, Drake J, Foster EP, Gao J, Herrera DA, Heo M, Hsu K, Hu J, Jackson D, Le C, Li Y, Lin R, Ma Z, Maddukuri A, Mirchandani S, Morton D, Nguyen T, O’Neill A, Scalise R, Seale D, Son V, Tian S, Tran E, Wang AE, Wu Y, Xie A, Yang J, Yin P, Zhang Y, Bastani O, Berseth G, Bohg J, Goldberg K, Gupta A, Gupta A, Jayaraman D, Lim JJ, Malik J, Martín-Martín R, Ramamoorthy S, Sadigh D, Song S, Wu J, Yip MC, Zhu Y, Kollar T, Levine S and Finn C (2024) DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset · 2024
Later among the works it cites.
URL https://arxiv.org/abs/2411.02479
Lambeta M, Wu T, Sengul A, Most VR, Black N, Sawyer K, Mercado R, Qi H, Sohn A, Taylor B, Tydingco N, Kammerer G, Stroud D, Khatha J, Jenkins K, Most K, Stein N, Chavira R, Craven-Bartle T, Sanchez E, Ding Y, Malik J and Calandra R (2024) Digitizing touch with an artificial multimodal fingertip · 2024
Later among the works it cites.
In: International Conference on Learning Representations
Li X, Liu M, Zhang H, Yu C, Xu J, Wu H, Cheang C, Jing Y, Zhang W, Liu H, Li H and Kong T (2024) Vision-language foundation models as effective robot imitators · 2024
Later among the works it cites.
In: 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids) . pp. 181–188
Liconti D, Toshimitsu Y and Katzschmann R (2024) Leveraging pretrained latent representations for few-shot imitation learning on an anthropomorphic robotic hand · 2024
Later among the works it cites.
arXiv preprint arXiv:2404.16823
Lin T, Zhang Y, Li Q, Qi H, Yi B, Levine S and Malik J (2024) Learning visuotactile skills with two multifingered hands · 2024
Later among the works it cites.
arXiv preprint arXiv:2405.17418
Liu J, Li C, Wang G, Lee L, Zhou K, Chen S, Xiong C, Ge J, Zhang R and Zhang S (2024) Self-corrected multimodal large language model for end-to-end robot manipulation · 2024
Later among the works it cites.
arXiv preprint arXiv:2409.11144
Lödige PW, Li MX and Lioutikov R (2024) Use the force, bot!–force-aware prodmp with event-based replanning · 2024
Later among the works it cites.
IEEE Robotics and Automation Letters
Ozdamar I, Sirintuna D, Arbaud R and Ajoudani A (2024) Pushing in the dark: A reactive pushing strategy for mobile robots using tactile feedback · 2024
Later among the works it cites.
In: 2024 IEEE International Conference on Robotics and Automation (ICRA) . pp. 6892–6903
O’Neill A, Rehman A, Maddukuri A, Gupta A, Padalkar A, Lee A, Pooley A, Gupta A, Mandlekar A, Jain A, Tung A, Bewley A, Herzog A, Irpan A, Khazatsky A, Rai A, Gupta A, Wang A, Singh A, Garg A, Kembhavi A, Xie A, Brohan A, Raffin A, Sharma A, Yavary A, Jain A, Balakrishna A, Wahid A, Burgess-Limerick B, Kim B, Schölkopf B, Wulfe B, Ichter B, Lu C, Xu C, Le C, Finn C, Wang C, Xu C, Chi C, Huang C, Chan C, Agia C, Pan C, Fu C, Devin C, Xu D, Morton D, Driess D, Chen D, Pathak D, Shah D, Büchler D, Jayaraman D, Kalashnikov D, Sadigh D, Johns E, Foster E, Liu F, Ceola F, Xia F, Zhao F, Stulp F, Zhou G, Sukhatme GS, Salhotra G, Yan G, Feng G, Schiavi G, Berseth G, Kahn G, Wang G, Su H, Fang HS, Shi H, Bao H, Ben Amor H, Christensen HI, Furuta H, Walke H, Fang H, Ha H, Mordatch I, Radosavovic I, Leal I, Liang J, Abou-Chakra J, Kim J, Drake J, Peters J, Schneider J, Hsu J, Bohg J, Bingham J, Wu J, Gao J, Hu J, Wu J, Wu J, Sun J, Luo J, Gu J, Tan J, Oh J, Wu J, Lu J, Yang J, Malik J, Silvério J, Hejna J, Booher J, Tompson J, Yang J, Salvador J, Lim JJ, Han J, Wang K, Rao K, Pertsch K, Hausman K, Go K, Gopalakrishnan K, Goldberg K, Byrne K, Oslund K, Kawaharazuka K, Black K, Lin K, Zhang K, Ehsani K, Lekkala K, Ellis K, Rana K, Srinivasan K, Fang K, Singh KP, Zeng KH, Hatch K, Hsu K, Itti L, Chen LY, Pinto L, Fei-Fei L, Tan L, Fan LJ, Ott L, Lee L, Weihs L, Chen M, Lepert M, Memmel M, Tomizuka M, Itkina M, Castro MG, Spero M, Du M, Ahn M, Yip MC, Zhang M, Ding M, Heo M, Srirama MK, Sharma M, Kim MJ, Kanazawa N, Hansen N, Heess N, Joshi NJ, Suenderhauf N, Liu N, Di Palo N, Shafiullah NMM, Mees O, Kroemer O, Bastani O, Sanketi PR, Miller PT, Yin P, Wohlhart P, Xu P, Fagan PD, Mitrano P, Sermanet P, Abbeel P, Sundaresan P, Chen Q, Vuong Q, Rafailov R, Tian R, Doshi R, Martín-Martín R, Baijal R, Scalise R, Hendrix R, Lin R, Qian R, Zhang R, Mendonca R, Shah R, Hoque R, Julian R, Bustamante S, Kirmani S, Levine S, Lin S, Moore S, Bahl S, Dass S, Sonawani S, Song S, Xu S, Haldar S, Karamcheti S, Adebola S, Guist S, Nasiriany S, Schaal S, Welker S, Tian S, Ramamoorthy S, Dasari S, Belkhale S, Park S, Nair S, Mirchandani S, Osa T, Gupta T, Harada T, Matsushima T, Xiao T, Kollar T, Yu T, Ding T, Davchev T, Zhao TZ, Armstrong T, Darrell T, Chung T, Jain V, Vanhoucke V, Zhan W, Zhou W, Burgard W, Chen X, Wang X, Zhu X, Geng X, Liu X, Liangwei X, Li X, Lu Y, Ma YJ, Kim Y, Chebotar Y, Zhou Y, Zhu Y, Wu Y, Xu Y, Wang Y, Bisk Y, Cho Y, Lee Y, Cui Y, Cao Y, Wu YH, Tang Y, Zhu Y, Zhang Y, Jiang Y, Li Y, Li Y, Iwasawa Y, Matsuo Y, Ma Z, Xu Z, Cui ZJ, Zhang Z and Lin Z (2024) Open x-embodiment: Robotic learning datasets and rt-x models : Open x-embodiment collaboration0 · 2024
Later among the works it cites.
In: Proceedings of Robotics: Science and Systems
Prasad A, Lin K, Wu J, Zhou L and Bohg J (2024) Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation · 2024
Later among the works it cites.
In: Robotics: Science and Systems
Shaw K, Agarwal A, Bahl S, Srirama MK, Kirchmeyer A, Kannan A, Sivakumar A and Pathak D (2024) Demonstrating learning from humans on open-source dexterous robot hands · 2024
Later among the works it cites.
In: Proceedings of Robotics: Science and Systems
Shi LX, Hu Z, Zhao TZ, Sharma A, Pertsch K, Luo J, Levine S and Finn C (2024) Yell At Your Robot: Improving On-the-Fly from Language Corrections · 2024
Later among the works it cites.
IEEE Robotics and Automation Letters 10(1): 240–247
Tsuji C, Coronado E, Osorio P and Venture G (2025) Adaptive contact-rich manipulation through few-shot imitation learning with force-torque feedback and pre-trained object representations · 2024
Later among the works it cites.
arXiv preprint arXiv:2408.04380
Urain J, Mandlekar A, Du Y, Shafiullah M, Xu D, Fragkiadaki K, Chalvatzaki G and Peters J (2024) Deep generative models in robotics: A survey on learning from multimodal demonstrations · 2024
Later among the works it cites.
arXiv preprint arXiv:2410.15123
Vedove MD, Abu-Dakka FJ, Palopoli L, Fontanelli D and Saveriano M (2024) Meshdmp: Motion planning on discrete manifolds using dynamic movement primitives · 2024
Later among the works it cites.
In: 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 537–544
Wan W, Zhu Y, Shah R and Zhu Y (2024) Lotus: Continual imitation learning for robot manipulation through unsupervised skill discovery · 2024
Later among the works it cites.
In: RSS 2024 Workshop: Data Generation for Robotics
Wang C, Shi H, Wang W, Zhang R, Fei-Fei L and Liu K (2024a) Dexcap: Scalable and portable mocap data collection system for dexterous manipulation · 2024
Later among the works it cites.
In: International Conference on Learning Representations
Wu H, Jing Y, Cheang C, Chen G, Xu J, Li X, Liu M, Li H and Kong T (2024) Unleashing large-scale video generative pre-training for visual robot manipulation · 2024
Later among the works it cites.
IEEE Robotics and Automation Letters 9(4): 3179–3186
Xiang G, Li S, Shuang F, Gao F and Yuan X (2024) Sc-airl: Share-critic in adversarial inverse reinforcement learning for long-horizon task · 2024
Later among the works it cites.
arXiv preprint arXiv:2403.04115
Yan G, Wu YH and Wang X (2024) Dnact: Diffusion guided multi-task 3d policy learning · 2024
Later among the works it cites.
In: 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 4804–4811
Yang J, Mark MS, Vu B, Sharma A, Bohg J and Finn C (2024) Robot fine-tuning made easy: Pre-training rewards and policies for autonomous real-world reinforcement learning · 2024
Later among the works it cites.
Frontiers in Neurorobotics 18: 1368243
Zang Y, Wang P, Zha F, Guo W, Li C and Sun L (2024) Human skill knowledge guided global trajectory policy reinforcement learning method · 2024
Later among the works it cites.
IEEE Robotics and Automation Letters 9(6): 5631–5638
Zhang H, Solak G, Lahr GJG and Ajoudani A (2024) Srl-vic: A variable stiffness-based safe reinforcement learning for contact-rich robotic tasks · 2024
Later among the works it cites.
arXiv preprint arXiv:2504.03515
An S, Meng Z, Tang C, Zhou Y, Liu T, Ding F, Zhang S, Mu Y, Song R, Zhang W et al. (2025) Dexterous manipulation through imitation learning: A survey · 2025
Closest in time.
In: The Thirteenth International Conference on Learning Representations
Barcellona L, Zadaianchuk A, Allegro D, Papa S, Ghidoni S and Gavves E (2025) Dream to manipulate: Compositional world models empowering robot imitation learning with imagination · 2025
Closest in time.
In: The Thirteenth International Conference on Learning Representations
Chen J, Yu C, Zhou X, Xu T, Mu Y, Hu M, Shao W, Wang Y, Li G and Shao L (2025) EMOS: Embodiment-aware heterogeneous multi-robot operating system with LLM agents · 2025
Closest in time.
In: The Thirteenth International Conference on Learning Representations
Duan J, Pumacay W, Kumar N, Wang YR, Tian S, Yuan W, Krishna R, Fox D, Mandlekar A and Guo Y (2025) AHA: A vision-language-model for detecting and reasoning over failures in robotic manipulation · 2025
Closest in time.
In: The Thirteenth International Conference on Learning Representations
Guo Y, Tang B, Akinola I, Fox D, Gupta A and Narang Y (2025) SRSA: Skill retrieval and adaptation for robotic assembly tasks · 2025
Closest in time.
arXiv preprint arXiv:2503.08548
Hao P, Zhang C, Li D, Cao X, Hao X, Cui S and Wang S (2025) Tla: Tactile-language-action model for contact-rich manipulation · 2025
Closest in time.
arXiv preprint arXiv:2502.13519
Korkmaz Y and Bıyık E (2025) Mile: Model-based intervention learning · 2025
Closest in time.
In: The Thirteenth International Conference on Learning Representations
Li Y, Deng Y, Zhang J, Jang J, Memmel M, Garrett CR, Ramos F, Fox D, Li A, Gupta A and Goyal A (2025) HAMSTER: Hierarchical action models for open-world robot manipulation · 2025
Closest in time.
In: 2025 IEEE International Conference on Robotics and Automation (ICRA)
Liu W, Wang J, Wang Y, Wang W and Lu C (2025) Forcemimic: Force-centric imitation learning with force-motion capture system for contact-rich manipulation · 2025
Closest in time.
In: The Thirteenth International Conference on Learning Representations
Memmel M, Berg J, Chen B, Gupta A and Francis J (2025) STRAP: Robot sub-trajectory retrieval for augmented policy learning · 2025
Closest in time.
Applied Intelligence 55(1): 1–15
Qian K, Yue Z and Bai J (2025) Hierarchical kernelized movement primitives for learning human-robot collaborative trajectories in referred object handover · 2025
Closest in time.
IEEE Access : 1–1
Tsuji T (2025) Mamba as a motion encoder for robotic imitation learning · 2025
Closest in time.
Mechatronics 107: 103307
Van Duong L (2025) A tactile reflex arc for physical human–robot interaction · 2025
Closest in time.
Scientific Reports 15(1): 9196
Xu B, Ud Din M and Hussain I (2025) Conditional variational auto encoder based dynamic motion for multitask imitation learning · 2025
Closest in time.
arXiv preprint arXiv:2502.20382
Yang L, Suh H, Zhao T, Graesdal BP, Kelestemur T, Wang J, Pang T and Tedrake R (2025) Physics-driven data generation for contact-rich manipulation via trajectory optimization · 2025
Closest in time.
In: The Thirteenth International Conference on Learning Representations
Zhao W, Ding P, Min Z, Gong Z, Bai S, Zhao H and Wang D (2025) VLAS: Vision-language-action model with speech instructions for customized robot manipulation · 2025
Closest in time.