Recurrent Neural Networks for driver activity anticipation via sensory-fusion architecture. - Ashesh Jain; Avi Singh; Hema Swetha Koppula; Shane Soh; Ashutosh Saxena (ICRA, 2016)

Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture. - Ashesh Jain; Hema Swetha Koppula; Shane Soh; Bharad Raghavan; Avi Singh; Ashutosh Saxena (CoRR, 2016)

Visual Dialog. - Abhishek Das; Satwik Kottur; Khushi Gupta; Avi Singh; Deshraj Yadav; José M. F. Moura; Devi Parikh; Dhruv Batra (CVPR, 2017)

GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images. - Avi Singh; Larry Yang; Sergey Levine (ICCV, 2017)

Few-Shot Goal Inference for Visuomotor Learning and Planning. - Annie Xie; Avi Singh; Sergey Levine; Chelsea Finn (CoRL, 2018)

Divide-and-Conquer Reinforcement Learning. - Dibya Ghosh; Avi Singh; Aravind Rajeswaran; Vikash Kumar; Sergey Levine (ICLR (Poster), 2018)

Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition. - Justin Fu; Avi Singh; Dibya Ghosh; Larry Yang; Sergey Levine (NeurIPS, 2018)

End-To-End Robotic Reinforcement Learning without Reward Engineering. - Avi Singh; Larry Yang; Chelsea Finn; Sergey Levine (Robotics: Science and Systems, 2019)

End-to-End Robotic Reinforcement Learning without Reward Engineering. - Avi Singh; Larry Yang; Kristian Hartikainen; Chelsea Finn; Sergey Levine (CoRR, 2019)

Visual Dialog. - Abhishek Das; Satwik Kottur; Khushi Gupta; Avi Singh; Deshraj Yadav; Stefan Lee; José M. F. Moura; Devi Parikh; Dhruv Batra (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Chaining Behaviors from Data with Model-Free Reinforcement Learning. - Avi Singh; Albert Yu 002; Jonathan Yang; Jesse Zhang; Aviral Kumar; Sergey Levine (CoRL, 2020)

The Ingredients of Real World Robotic Reinforcement Learning. - Henry Zhu; Justin Yu; Abhishek Gupta 004; Dhruv Shah; Kristian Hartikainen; Avi Singh; Vikash Kumar; Sergey Levine (ICLR, 2020)

Scalable Multi-Task Imitation Learning with Autonomous Improvement. - Avi Singh; Eric Jang; Alexander Irpan; Daniel Kappler; Murtaza Dalal; Sergey Levine; Mohi Khansari; Chelsea Finn (ICRA, 2020)

COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning. - Avi Singh; Albert Yu 002; Jonathan Yang; Jesse Zhang; Aviral Kumar; Sergey Levine (CoRR, 2020)

Parrot: Data-Driven Behavioral Priors for Reinforcement Learning. - Avi Singh; Huihan Liu; Gaoyue Zhou; Albert Yu 002; Nicholas Rhinehart; Sergey Levine (ICLR, 2021)

Real World Robot Learning: Learned Rewards, Offline Datasets and Skill Re-Use - Avi Singh (2021)

i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops. - Saminda Wishwajith Abeyruwan; Laura Graesser; David B. D'Ambrosio; Avi Singh; Anish Shankar; Alex Bewley; Deepali Jain; Krzysztof Marcin Choromanski; Pannag R. Sanketi (CoRL, 2022)

Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning. - Homer Walke; Jonathan Yang; Albert Yu 002; Aviral Kumar; Jedrzej Orbik; Avi Singh; Sergey Levine (CoRL, 2022)

Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. - David Brandfonbrener; Stephen Tu; Avi Singh; Stefan Welker; Chad Boodoo; Nikolai Matni; Jake Varley (ICRA, 2023)

Robotic Table Tennis: A Case Study into a High Speed Learning System. - David B. D'Ambrosio; Navdeep Jaitly; Vikas Sindhwani; Ken Oslund; Peng Xu 010; Nevena Lazic; Anish Shankar; Tianli Ding; Jonathan Abelian; Erwin Coumans; Gus Kouretas; Thinh Nguyen; et al. (Robotics: Science and Systems, 2023)

Robotic Table Tennis: A Case Study into a High Speed Learning System. - David B. D'Ambrosio; Jonathan Abelian; Saminda Abeyruwan; Michael Ahn; Alex Bewley; Justin Boyd; Krzysztof Choromanski; Omar Cortes; Erwin Coumans; Tianli Ding; Wenbo Gao; Laura Graesser; et al. (CoRR, 2023)

Improving Large Language Model Fine-tuning for Solving Math Problems. - Yixin Liu; Avi Singh; C. Daniel Freeman; John D. Co-Reyes; Peter J. Liu (CoRR, 2023)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei Zhang; Bernd Bohnet; Luis Rosias; Stephanie C. Y. Chan; Biao Zhang; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; et al. (NeurIPS, 2024)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei M. Zhang; Bernd Bohnet; Stephanie Chan 003; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; Feryal M. P. Behbahani; Aleksandra Faust; et al. (CoRR, 2024)

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. - Avi Singh; John D. Co-Reyes; Rishabh Agarwal; Ankesh Anand; Piyush Patil; Xavier Garcia; Peter J. Liu; James Harrison; Jaehoon Lee 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)

Training Language Models to Self-Correct via Reinforcement Learning. - Aviral Kumar; Vincent Zhuang; Rishabh Agarwal; Yi Su; John D. Co-Reyes; Avi Singh; Kate Baumli; Shariq Iqbal; Colton Bishop; Rebecca Roelofs; Lei M. Zhang; Kay McKinney; et al. (ICLR, 2025)