Behind the Article: Recognizing Dialog Acts in Wikipedia Talk Pages. - Oliver Ferschke; Iryna Gurevych; Yevgen Chebotar (EACL, 2012)

Learning robot tactile sensing for object manipulation. - Yevgen Chebotar; Oliver Kroemer; Jan Peters 001 (IROS, 2014)

Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor. - Zhe Su; Karol Hausman; Yevgen Chebotar; Artem Molchanov; Gerald E. Loeb; Gaurav S. Sukhatme; Stefan Schaal (Humanoids, 2015)

Distilling Knowledge from Ensembles of Neural Networks for Speech Recognition. - Yevgen Chebotar; Austin Waters (INTERSPEECH, 2016)

Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning. - Yevgen Chebotar; Karol Hausman; Zhe Su; Gaurav S. Sukhatme; Stefan Schaal (IROS, 2016)

Generalizing Regrasping with Supervised Policy Learning. - Yevgen Chebotar; Karol Hausman; Oliver Kroemer; Gaurav S. Sukhatme; Stefan Schaal (ISER, 2016)

Regrasping Using Tactile Perception and Supervised Policy Learning. - Yevgen Chebotar; Karol Hausman; Oliver Kroemer; Gaurav S. Sukhatme; Stefan Schaal (AAAI Spring Symposia, 2017)

Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. - Yevgen Chebotar; Karol Hausman; Marvin Zhang; Gaurav S. Sukhatme; Stefan Schaal; Sergey Levine (ICML, 2017)

Path integral guided policy search. - Yevgen Chebotar; Mrinal Kalakrishnan; Ali Yahya; Adrian Li; Stefan Schaal; Sergey Levine (ICRA, 2017)

Collective robot reinforcement learning with distributed asynchronous guided policy search. - Ali Yahya; Adrian Li; Mrinal Kalakrishnan; Yevgen Chebotar; Sergey Levine (IROS, 2017)

Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets. - Karol Hausman; Yevgen Chebotar; Stefan Schaal; Gaurav S. Sukhatme; Joseph J. Lim (NIPS, 2017)

Time-Contrastive Networks: Self-Supervised Learning from Video. - Pierre Sermanet; Corey Lynch; Yevgen Chebotar; Jasmine Hsu; Eric Jang; Stefan Schaal; Sergey Levine (ICRA, 2018)

Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience. - Yevgen Chebotar; Ankur Handa; Viktor Makoviychuk; Miles Macklin; Jan Issac; Nathan D. Ratliff; Dieter Fox (ICRA, 2019)

Learning Latent Space Dynamics for Tactile Servoing. - Giovanni Sutanto; Nathan D. Ratliff; Balakumar Sundaralingam; Yevgen Chebotar; Zhe Su; Ankur Handa; Dieter Fox (ICRA, 2019)

Meta-Learning via Learned Loss. - Yevgen Chebotar; Artem Molchanov; Sarah Bechtle; Ludovic Righetti; Franziska Meier; Gaurav S. Sukhatme (CoRR, 2019)

Meta Learning via Learned Loss. - Sarah Bechtle; Artem Molchanov; Yevgen Chebotar; Edward Grefenstette; Ludovic Righetti; Gaurav S. Sukhatme; Franziska Meier (ICPR, 2020)

Scaling Up Multi-Task Robotic Reinforcement Learning. - Dmitry Kalashnikov; Jake Varley; Yevgen Chebotar; Benjamin Swanson; Rico Jonschkowski; Chelsea Finn; Sergey Levine; Karol Hausman (CoRL, 2021)

AW-Opt: Learning Robotic Skills with Imitation andReinforcement at Scale. - Yao Lu 006; Karol Hausman; Yevgen Chebotar; Mengyuan Yan; Eric Jang; Alexander Herzog; Ted Xiao; Alex Irpan; Mohi Khansari; Dmitry Kalashnikov; Sergey Levine (CoRL, 2021)

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. - Yevgen Chebotar; Karol Hausman; Yao Lu 006; Ted Xiao; Dmitry Kalashnikov; Jacob Varley; Alex Irpan; Benjamin Eysenbach; Ryan Julian; Chelsea Finn; Sergey Levine (ICML, 2021)

Visionary: Vision architecture discovery for robot learning. - Iretiayo Akinola; Anelia Angelova; Yao Lu 006; Yevgen Chebotar; Dmitry Kalashnikov; Jacob Varley; Julian Ibarz; Michael S. Ryoo (ICRA, 2021)

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. - Tianhe Yu; Aviral Kumar; Yevgen Chebotar; Karol Hausman; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. - Yevgen Chebotar; Karol Hausman; Yao Lu 006; Ted Xiao; Dmitry Kalashnikov; Jake Varley; Alex Irpan; Benjamin Eysenbach; Ryan Julian; Chelsea Finn; Sergey Levine (CoRR, 2021)

MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale. - Dmitry Kalashnikov; Jacob Varley; Yevgen Chebotar; Benjamin Swanson; Rico Jonschkowski; Chelsea Finn; Sergey Levine; Karol Hausman (CoRR, 2021)

AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale. - Yao Lu 006; Karol Hausman; Yevgen Chebotar; Mengyuan Yan; Eric Jang; Alexander Herzog; Ted Xiao; Alex Irpan; Mohi Khansari; Dmitry Kalashnikov; Sergey Levine (CoRR, 2021)

Inner Monologue: Embodied Reasoning through Planning with Language Models. - Wenlong Huang; Fei Xia 002; Ted Xiao; Harris Chan; Jacky Liang; Pete Florence; Andy Zeng 001; Jonathan Tompson; Igor Mordatch; Yevgen Chebotar; Pierre Sermanet; Tomas Jackson; et al. (CoRL, 2022)

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. - Brian Ichter; Anthony Brohan; Yevgen Chebotar; Chelsea Finn; Karol Hausman; Alexander Herzog; Daniel Ho; Julian Ibarz; Alex Irpan; Eric Jang; Ryan Julian; Dmitry Kalashnikov; et al. (CoRL, 2022)

How to Leverage Unlabeled Data in Offline Reinforcement Learning. - Tianhe Yu; Aviral Kumar; Yevgen Chebotar; Karol Hausman; Chelsea Finn; Sergey Levine (ICML, 2022)

DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning. - Quan Vuong; Aviral Kumar; Sergey Levine; Yevgen Chebotar (NeurIPS, 2022)

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. - Michael Ahn; Anthony Brohan; Noah Brown; Yevgen Chebotar; Omar Cortes; Byron David; Chelsea Finn; Keerthana Gopalakrishnan; Karol Hausman; Alexander Herzog; Daniel Ho; Jasmine Hsu; et al. (CoRR, 2022)

Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints. - Anikait Singh; Aviral Kumar; Quan Vuong; Yevgen Chebotar; Sergey Levine (CoRR, 2022)

Dual Generator Offline Reinforcement Learning. - Quan Vuong; Aviral Kumar; Sergey Levine; Yevgen Chebotar (CoRR, 2022)

Supervised learning and reinforcement learning of feedback models for reactive behaviors: Tactile feedback testbed. - Giovanni Sutanto; Katharina Rombach; Yevgen Chebotar; Zhe Su; Stefan Schaal; Gaurav S. Sukhatme; Franziska Meier (Int. J. Robotics Res., 2022)

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. - Yevgen Chebotar; Quan Vuong; Karol Hausman; Fei Xia 002; Yao Lu 006; Alex Irpan; Aviral Kumar; Tianhe Yu; Alexander Herzog; Karl Pertsch; Keerthana Gopalakrishnan; Julian Ibarz; et al. (CoRL, 2023)

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. - Brianna Zitkovich; Tianhe Yu; Sichun Xu; Peng Xu 010; Ted Xiao; Fei Xia 002; Jialin Wu; Paul Wohlhart; Stefan Welker; Ayzaan Wahid; Quan Vuong; Vincent Vanhoucke; et al. (CoRL, 2023)

PaLM-E: An Embodied Multimodal Language Model. - Danny Driess; Fei Xia 002; Mehdi S. M. Sajjadi; Corey Lynch; Aakanksha Chowdhery; Brian Ichter; Ayzaan Wahid; Jonathan Tompson; Quan Vuong; Tianhe Yu; Wenlong Huang; Yevgen Chebotar; et al. (ICML, 2023)

ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints. - Anikait Singh; Aviral Kumar; Quan Vuong; Yevgen Chebotar; Sergey Levine (NeurIPS, 2023)

RT-1: Robotics Transformer for Real-World Control at Scale. - Anthony Brohan; Noah Brown; Justice Carbajal; Yevgen Chebotar; Joseph Dabis; Chelsea Finn; Keerthana Gopalakrishnan; Karol Hausman; Alexander Herzog; Jasmine Hsu; Julian Ibarz; Brian Ichter; et al. (Robotics: Science and Systems, 2023)

Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators. - Alexander Herzog; Kanishka Rao; Karol Hausman; Yao Lu 006; Paul Wohlhart; Mengyuan Yan; Jessica Lin; Montserrat Gonzalez Arenas; Ted Xiao; Daniel Kappler; Daniel Ho; Jarek Rettinghouse; et al. (Robotics: Science and Systems, 2023)

Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators. - Alexander Herzog; Kanishka Rao; Karol Hausman; Yao Lu 006; Paul Wohlhart; Mengyuan Yan; Jessica Lin; Montserrat Gonzalez Arenas; Ted Xiao; Daniel Kappler; Daniel Ho; Jarek Rettinghouse; et al. (CoRR, 2023)

Robotic Offline RL from Internet Videos via Value-Function Pre-Training. - Chethan Bhateja; Derek Guo; Dibya Ghosh; Anikait Singh; Manan Tomar; Quan Vuong; Yevgen Chebotar; Sergey Levine; Aviral Kumar (CoRR, 2023)

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL. - Jesse Farebrother; Jordi Orbay; Quan Vuong; Adrien Ali Taïga; Yevgen Chebotar; Ted Xiao; Alex Irpan; Sergey Levine; Pablo Samuel Castro; Aleksandra Faust; Aviral Kumar; Rishabh Agarwal (ICML, 2024)

Robotic Offline RL from Internet Videos via Value-Function Learning. - Chethan Bhateja; Derek Guo; Dibya Ghosh; Anikait Singh; Manan Tomar; Quan Vuong; Yevgen Chebotar; Sergey Levine; Aviral Kumar (ICRA, 2024)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. - Abby O'Neill; Abdul Rehman; Abhiram Maddukuri; Abhishek Gupta 004; Abhishek Padalkar; Abraham Lee; Acorn Pooley; Agrim Gupta; Ajay Mandlekar; Ajinkya Jain; Albert Tung; Alex Bewley; et al. (ICRA, 2024)

RT-H: Action Hierarchies using Language. - Suneel Belkhale; Tianli Ding; Ted Xiao; Pierre Sermanet; Quan Vuong; Jonathan Tompson; Yevgen Chebotar; Debidatta Dwibedi; Dorsa Sadigh (Robotics: Science and Systems, 2024)

RT-H: Action Hierarchies Using Language. - Suneel Belkhale; Tianli Ding; Ted Xiao; Pierre Sermanet; Quon Vuong; Jonathan Tompson; Yevgen Chebotar; Debidatta Dwibedi; Dorsa Sadigh (CoRR, 2024)