Self-Supervised Policy Adaptation during Deployment. - Nicklas Hansen 001; Yu Sun 020; Pieter Abbeel; Alexei A. Efros; Lerrel Pinto; Xiaolong Wang 004 (CoRR, 2020)

Self-Supervised Policy Adaptation during Deployment. - Nicklas Hansen 001; Rishabh Jangir; Yu Sun 020; Guillem Alenyà; Pieter Abbeel; Alexei A. Efros; Lerrel Pinto; Xiaolong Wang 004 (ICLR, 2021)

Generalization in Reinforcement Learning by Soft Data Augmentation. - Nicklas Hansen 001; Xiaolong Wang 004 (ICRA, 2021)

Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation. - Nicklas Hansen 001; Hao Su 001; Xiaolong Wang 004 (NeurIPS, 2021)

Graph Inverse Reinforcement Learning from Diverse Videos. - Sateesh Kumar; Jonathan Zamora; Nicklas Hansen 001; Rishabh Jangir; Xiaolong Wang 004 (CoRL, 2022)

Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers. - Ruihan Yang; Minghao Zhang; Nicklas Hansen 001; Huazhe Xu; Xiaolong Wang 004 (ICLR, 2022)

Temporal Difference Learning for Model Predictive Control. - Nicklas Hansen 001; Hao Su 001; Xiaolong Wang 004 (ICML, 2022)

Inverse Reinforcement Learning from Diverse Third-Person Videos via Graph Abstraction. - Sateesh Kumar; Jonathan Zamora; Nicklas Hansen 001; Rishabh Jangir; Xiaolong Wang 004 (CoRR, 2022)

Look Closer: Bridging Egocentric and Third-Person Views With Transformers for Robotic Manipulation. - Rishabh Jangir; Nicklas Hansen 001; Sambaran Ghosal; Mohit Jain; Xiaolong Wang 004 (IEEE Robotics Autom. Lett., 2022)

Finetuning Offline World Models in the Real World. - Yunhai Feng; Nicklas Hansen 001; Ziyan Xiong; Chandramouli Rajagopalan; Xiaolong Wang 004 (CoRL, 2023)

GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields. - Yanjie Ze; Ge Yan; Yueh-Hua Wu; Annabella Macaluso; Yuying Ge; Jianglong Ye; Nicklas Hansen 001; Li Erran Li; Xiaolong Wang 004 (CoRL, 2023)

MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations. - Nicklas Hansen 001; Yixin Lin; Hao Su 001; Xiaolong Wang 004; Vikash Kumar; Aravind Rajeswaran (ICLR, 2023)

On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning. - Yifan Xu 009; Nicklas Hansen 001; Zirui Wang; Yung-Chieh Chan; Hao Su 001; Zhuowen Tu (ICLR, 2023)

On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline. - Nicklas Hansen 001; Zhecheng Yuan; Yanjie Ze; Tongzhou Mu; Aravind Rajeswaran; Hao Su 001; Huazhe Xu; Xiaolong Wang 004 (ICML, 2023)

Visual Reinforcement Learning With Self-Supervised 3D Representations. - Yanjie Ze; Nicklas Hansen 001; Yinbo Chen; Mohit Jain; Xiaolong Wang 004 (IEEE Robotics Autom. Lett., 2023)

TD-MPC2: Scalable, Robust World Models for Continuous Control. - Nicklas Hansen 001; Hao Su 001; Xiaolong Wang 004 (ICLR, 2024)

MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation. - Patrick Lancaster; Nicklas Hansen 001; Aravind Rajeswaran; Vikash 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)

A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data. - Adrian Remonda; Nicklas Hansen 001; Ayoub Raji; Nicola Musiu; Marko Bertogna; Eduardo E. Veas; Xiaolong Wang 004 (NeurIPS, 2024)

A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning. - Abdulaziz Almuzairee; Nicklas Hansen 001; Henrik I. Christensen (RLJ, 2024)

PWM: Policy Learning with Large World Models. - Ignat Georgiev; Varun Giridhar; Nicklas Hansen 001; Animesh Garg (CoRR, 2024)

Hierarchical World Models as Visual Whole-Body Humanoid Controllers. - Nicklas Hansen 001; Jyothir S. V; Vlad Sobal; Yann LeCun; Xiaolong Wang 004; Hao Su 001 (ICLR, 2025)

PWM: Policy Learning with Multi-Task World Models. - Ignat Georgiev; Varun Giridhar; Nicklas Hansen 001; Animesh Garg (ICLR, 2025)

Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning. - Adrià López Escoriza; Nicklas Hansen 001; Stone Tao; Tongzhou Mu; Hao Su 001 (CoRR, 2025)