The Best of Both Worlds: Learning Geometry-based 6D Object Pose Estimation. - Omid Hosseini Jafari; Siva Karthik Mustikovela; Karl Pertsch; Eric Brachmann; Carsten Rother (CoRR, 2017)

iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects. - Omid Hosseini Jafari; Siva Karthik Mustikovela; Karl Pertsch; Eric Brachmann; Carsten Rother (ACCV (3), 2018)

Unsupervised Learning of Sensorimotor Affordances by Stochastic Future Prediction. - Oleh Rybkin; Karl Pertsch; Andrew Jaegle; Konstantinos G. Derpanis; Kostas Daniilidis (CoRR, 2018)

Learning what you can do before doing anything. - Oleh Rybkin; Karl Pertsch; Konstantinos G. Derpanis; Kostas Daniilidis; Andrew Jaegle (ICLR (Poster), 2019)

KeyIn: Discovering Subgoal Structure with Keyframe-based Video Prediction. - Karl Pertsch; Oleh Rybkin; Jingyun Yang; Konstantinos G. Derpanis; Joseph J. Lim; Kostas Daniilidis; Andrew Jaegle (CoRR, 2019)

Accelerating Reinforcement Learning with Learned Skill Priors. - Karl Pertsch; Youngwoon Lee; Joseph J. Lim (CoRL, 2020)

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments. - Jun Yamada; Youngwoon Lee; Gautam Salhotra; Karl Pertsch; Max Pflueger; Gaurav S. Sukhatme; Joseph J. Lim; Peter Englert (CoRL, 2020)

Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning. - Karl Pertsch; Oleh Rybkin; Jingyun Yang; Shenghao Zhou; Konstantinos G. Derpanis; Kostas Daniilidis; Joseph J. Lim; Andrew Jaegle (L4DC, 2020)

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. - Karl Pertsch; Oleh Rybkin; Frederik Ebert; Shenghao Zhou; Dinesh Jayaraman; Chelsea Finn; Sergey Levine (NeurIPS, 2020)

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. - Karl Pertsch; Oleh Rybkin; Frederik Ebert; Chelsea Finn; Dinesh Jayaraman; Sergey Levine (CoRR, 2020)

Demonstration-Guided Reinforcement Learning with Learned Skills. - Karl Pertsch; Youngwoon Lee; Yue Wu; Joseph J. Lim (CoRL, 2021)

Cross-Domain Transfer via Semantic Skill Imitation. - Karl Pertsch; Ruta Desai; Vikash Kumar; Franziska Meier; Joseph J. Lim; Dhruv Batra; Akshara Rai (CoRL, 2022)

Skill-based Meta-Reinforcement Learning. - Taewook Nam; Shao-Hua Sun; Karl Pertsch; Sung Ju Hwang; Joseph J. Lim (ICLR, 2022)

Task-Induced Representation Learning. - Jun Yamada; Karl Pertsch; Anisha Gunjal; Joseph J. Lim (ICLR, 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)

Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance. - Jesse Zhang; Jiahui Zhang; Karl Pertsch; Ziyi Liu; Xiang Ren; Minsuk Chang; Shao-Hua Sun; Joseph J. Lim (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)

RoboCLIP: One Demonstration is Enough to Learn Robot Policies. - Sumedh Sontakke; Jesse Zhang; Sébastien M. R. Arnold; Karl Pertsch; Erdem Biyik; Dorsa Sadigh; Chelsea Finn; Laurent Itti (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)

PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection. - Shivin Dass; Karl Pertsch; Hejia Zhang; Youngwoon Lee; Joseph J. Lim; Stefanos Nikolaidis (Robotics: Science and Systems, 2023)

LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers. - Taewook Nam; Juyong Lee; Jesse Zhang; Sung Ju Hwang; Joseph J. Lim; Karl Pertsch (CoRR, 2023)

ReMix: Optimizing Data Mixtures for Large Scale Imitation Learning. - Joey Hejna; Chethan Anand Bhateja; Yichen Jiang; Karl Pertsch; Dorsa Sadigh (CoRL, 2024)

OpenVLA: An Open-Source Vision-Language-Action Model. - Moo Jin Kim; Karl Pertsch; Siddharth Karamcheti; Ted Xiao; Ashwin Balakrishna; Suraj Nair 003; Rafael Rafailov; Ethan Paul Foster; Pannag R. Sanketi; Quan Vuong; Thomas Kollar; Benjamin Burchfiel; et al. (CoRL, 2024)

Evaluating Real-World Robot Manipulation Policies in Simulation. - Xuanlin Li; Kyle Hsu; Jiayuan Gu; Oier Mees; Karl Pertsch; Homer Rich Walke; Chuyuan Fu; Ishikaa Lunawat; Isabel Sieh; Sean Kirmani; Sergey Levine; Jiajun Wu 001; et al. (CoRL, 2024)

Robotic Control via Embodied Chain-of-Thought Reasoning. - Michal Zawalski; William Chen; Karl Pertsch; Oier Mees; Chelsea Finn; Sergey Levine (CoRL, 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)

SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling. - Jesse Zhang; Karl Pertsch; Jiahui Zhang; Joseph J. Lim (ICRA, 2024)

Octo: An Open-Source Generalist Robot Policy. - Dibya Ghosh; Homer Rich Walke; Karl Pertsch; Kevin Black; Oier Mees; Sudeep Dasari; Joey Hejna; Tobias Kreiman; Charles Xu 003; Jianlan Luo; You Liang Tan; Lawrence Yunliang Chen; et al. (Robotics: Science and Systems, 2024)

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset. - Alexander Khazatsky; Karl Pertsch; Suraj Nair 003; Ashwin Balakrishna; Sudeep Dasari; Siddharth Karamcheti; Soroush Nasiriany; Mohan Kumar Srirama; Lawrence Yunliang Chen; Kirsty Ellis; Peter David Fagan; Joey Hejna; et al. (Robotics: Science and Systems, 2024)

Yell At Your Robot: Improving On-the-Fly from Language Corrections. - Lucy Xiaoyang Shi; Zheyuan Hu 003; Tony Z. Zhao; Archit Sharma; Karl Pertsch; Jianlan Luo; Sergey Levine; Chelsea Finn (Robotics: Science and Systems, 2024)

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset. - Alexander Khazatsky; Karl Pertsch; Suraj Nair 003; Ashwin Balakrishna; Sudeep Dasari; Siddharth Karamcheti; Soroush Nasiriany; Mohan Kumar Srirama; Lawrence Yunliang Chen; Kirsty Ellis; Peter David Fagan; Joey Hejna; et al. (CoRR, 2024)

Octo: An Open-Source Generalist Robot Policy. - Octo Model Team; Dibya Ghosh; Homer Walke; Karl Pertsch; Kevin Black; Oier Mees; Sudeep Dasari; Joey Hejna; Tobias Kreiman; Charles Xu 003; Jianlan Luo; You Liang Tan; et al. (CoRR, 2024)

OpenVLA: An Open-Source Vision-Language-Action Model. - Moo Jin Kim; Karl Pertsch; Siddharth Karamcheti; Ted Xiao; Ashwin Balakrishna; Suraj Nair 003; Rafael Rafailov; Ethan Paul Foster; Grace Lam; Pannag Sanketi; Quan Vuong; Thomas Kollar; et al. (CoRR, 2024)

Affordance-Guided Reinforcement Learning via Visual Prompting. - Olivia Y. Lee; Annie Xie; Kuan Fang; Karl Pertsch; Chelsea Finn (CoRR, 2024)

Re-Mix: Optimizing Data Mixtures for Large Scale Imitation Learning. - Joey Hejna; Chethan Bhateja; Yichen Jiang; Karl Pertsch; Dorsa Sadigh (CoRR, 2024)

π0: A Vision-Language-Action Flow Model for General Robot Control. - Kevin Black; Noah Brown; Danny Driess; Adnan Esmail; Michael Equi; Chelsea Finn; Niccolo Fusai; Lachy Groom; Karol Hausman; Brian Ichter; Szymon Jakubczak; Tim Jones; et al. (CoRR, 2024)

FAST: Efficient Action Tokenization for Vision-Language-Action Models. - Karl Pertsch; Kyle Stachowicz; Brian Ichter; Danny Driess; Suraj Nair 003; Quan Vuong; Oier Mees; Chelsea Finn; Sergey Levine (CoRR, 2025)

Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models. - Lucy Xiaoyang Shi; Brian Ichter; Michael Equi; Liyiming Ke; Karl Pertsch; Quan Vuong; James Tanner; Anna Walling; Haohuan Wang; Niccolo Fusai; Adrian Li-Bell; Danny Driess; et al. (CoRR, 2025)

AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World. - Zhiyuan Zhou; Pranav Atreya; You Liang Tan; Karl Pertsch; Sergey Levine (CoRR, 2025)

π0.5: a Vision-Language-Action Model with Open-World Generalization. - Physical Intelligence; Kevin Black; Noah Brown; James Darpinian; Karan Dhabalia; Danny Driess; Adnan Esmail; Michael Equi; Chelsea Finn; Niccolo Fusai; Manuel Y. Galliker; Dibya Ghosh; et al. (CoRR, 2025)

Training Strategies for Efficient Embodied Reasoning. - William Chen; Suneel Belkhale; Suvir Mirchandani; Oier Mees; Danny Driess; Karl Pertsch; Sergey Levine (CoRR, 2025)

Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better. - Danny Driess; Jost Tobias Springenberg; Brian Ichter; Lili Yu; Adrian Li-Bell; Karl Pertsch; Allen Z. Ren; Homer Walke; Quan Vuong; Lucy Xiaoyang Shi; Sergey Levine (CoRR, 2025)

RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies. - Pranav Atreya; Karl Pertsch; Tony Lee; Moo Jin Kim; Arhan Jain; Artur Kuramshin; Clemens Eppner; Cyrus Neary; Edward S. Hu; Fabio Ramos 001; Jonathan Tremblay; Kanav Arora; et al. (CoRR, 2025)