Backprop KF: Learning Discriminative Deterministic State Estimators. - Tuomas Haarnoja; Anurag Ajay; Sergey Levine; Pieter Abbeel (NIPS, 2016)

Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states. - William Montgomery; Anurag Ajay; Chelsea Finn; Pieter Abbeel; Sergey Levine (ICRA, 2017)

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing. - Anurag Ajay; Jiajun Wu 001; Nima Fazeli; Maria Bauzá 001; Leslie Pack Kaelbling; Joshua B. Tenenbaum; Alberto Rodriguez 003 (IROS, 2018)

Combining Physical Simulators and Object-Based Networks for Control. - Anurag Ajay; Maria Bauzá 001; Jiajun Wu 001; Nima Fazeli; Joshua B. Tenenbaum; Alberto Rodriguez 003; Leslie Pack Kaelbling (ICRA, 2019)

Learning to Navigate Endoscopic Capsule Robots. - Mehmet Turan; Yasin Almalioglu; Hunter B. Gilbert; Faisal Mahmood 001; Nicholas J. Durr; Helder Araújo; Alp Eren Sari; Anurag Ajay; Metin Sitti (IEEE Robotics Autom. Lett., 2019)

Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners. - Nima Fazeli; Anurag Ajay; Alberto Rodriguez 003 (ICRA, 2020)

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. - Anurag Ajay; Aviral Kumar; Pulkit Agrawal 001; Sergey Levine; Ofir Nachum (ICLR, 2021)

Overcoming The Spectral Bias of Neural Value Approximation. - Ge Yang 003; Anurag Ajay; Pulkit Agrawal 001 (ICLR, 2022)

Offline RL Policies Should Be Trained to be Adaptive. - Dibya Ghosh; Anurag Ajay; Pulkit Agrawal 001; Sergey Levine (ICML, 2022)

Distributionally Adaptive Meta Reinforcement Learning. - Anurag Ajay; Abhishek Gupta 004; Dibya Ghosh; Sergey Levine; Pulkit Agrawal 001 (NeurIPS, 2022)

Is Conditional Generative Modeling all you need for Decision Making? - Anurag Ajay; Yilun Du; Abhi Gupta; Joshua B. Tenenbaum; Tommi S. Jaakkola; Pulkit Agrawal 001 (ICLR, 2023)

Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. - Zechu Li; Tao Chen 046; Zhang-Wei Hong; Anurag Ajay; Pulkit Agrawal 001 (ICML, 2023)

Statistical Learning under Heterogenous Distribution Shift. - Max Simchowitz; Anurag Ajay; Pulkit Agrawal 001; Akshay Krishnamurthy (ICML, 2023)

Compositional Foundation Models for Hierarchical Planning. - Anurag Ajay; Seungwook Han; Yilun Du; Shuang Li 013; Abhi Gupta; Tommi S. Jaakkola; Joshua B. Tenenbaum; Leslie Pack Kaelbling; Akash Srivastava; Pulkit Agrawal 001 (NeurIPS, 2023)

Multi-Year On-Farm Trial Data on the Performance of Long- and Short-Duration Wheat Varieties against Sowing Dates in the Eastern Indo-Gangetic Plain of India. - Anurag Ajay; Madhulika Singh; Subhajit Patra; Harshit Ranjan; Ajay Pundir; Shishpal Poonia; Anurag Kumar; Deepak K. Singh; Pankaj Kumar; Moben Ignatius; Prabhat Kumar; Sonam R. Sherpa; et al. (Data, 2023)

OpenEQA: Embodied Question Answering in the Era of Foundation Models. - Arjun Majumdar; Anurag Ajay; Xiaohan Zhang 002; Pranav Putta; Sriram Yenamandra; Mikael Henaff; Sneha Silwal; Paul McVay; Oleksandr Maksymets; Sergio Arnaud; Karmesh Yadav; Qiyang Li; et al. (CVPR, 2024)

Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient. - Steven Li; Rickmer Krohn; Tao Chen 046; Anurag Ajay; Pulkit Agrawal 001; Georgia Chalvatzaki (NeurIPS, 2024)

An Introduction to Vision-Language Modeling. - Florian Bordes; Richard Yuanzhe Pang; Anurag Ajay; Alexander C. Li; Adrien Bardes; Suzanne Petryk; Oscar Mañas; Zhiqiu Lin; Anas Mahmoud 002; Bargav Jayaraman; Mark Ibrahim; Melissa Hall; et al. (CoRR, 2024)

Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient. - Zechu Li; Rickmer Krohn; Tao Chen 046; Anurag Ajay; Pulkit Agrawal 001; Georgia Chalvatzaki (CoRR, 2024)