Guanzhi Wang

ASPIRE: Agentic /Skills Discovery for Robotics

Runyu Lu*^†, Yubo Wu*, Ethan Kou*, Max Fu, Wenli Xiao, Ajay Mandlekar, Yinzhen Xu, Guanya Shi, Ken Goldberg, Ang Chen, Mosharaf Chowdhury, Yuke Zhu^†, Linxi "Jim" Fan^†, Guanzhi Wang^†

arXiv preprint, 2026

Paper Project Page Code

We introduce ASPIRE, a continual learning system that writes and refines robot control programs, accumulating reusable skills that transfer across tasks, environments, and embodiments.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

Wenli Xiao*, Jia Xie*, Tonghe Zhang*, Haotian Lin*, Letian "Max" Fu, Haoru Xue, Jalen Lu, Yi Yang, Cunxi Dai, Zi Wang, Jimmy Wu, Guanzhi Wang, S. Shankar Sastry, Ken Goldberg, Linxi "Jim" Fan^†, Yuke Zhu^†, Guanya Shi^†

Conference on Robot Learning (CoRL), 2026

Paper Project Page Code

We introduce ENPIRE, a framework that lets coding agents autonomously improve real-world robot policies through automatic resets, outcome verification, and iterative experimentation.

GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness for Variational Automation Tasks

Kaiyuan Chen, Shuangyu Xie, Letian Fu, Justin Yu, William Pacini, Sandeep Bajamahal, Hudson Kim, Jaimyn Drake, Daehwa Kim, Haoru Xue, Jonathan Francis, Christian Juette, Peter Schaldenbrand, Muhammet Yunus Seker, Ruwan Wickramarachchi, Uksang Yoo, Guanzhi Wang, Adithyavairavan Murali, Balakumar Sundaralingam, S. Shankar Sastry, Spencer Huang, Yuke Zhu, Linxi "Jim" Fan, Ken Goldberg

Conference on Robot Learning (CoRL), 2026

Paper Project Page Code

We introduce GaP, a multi-agent system that builds executable robot policy graphs and refines them through simulation rehearsal for tasks with varying object geometry and poses.

CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation

Max Fu*, Justin Yu*, Karim El-Refai*, Ethan Kou*, Haoru Xue*, Huang Huang, Wenli Xiao, Guanzhi Wang, Fei-Fei Li, Guanya Shi, Jiajun Wu, Shankar Sastry, Yuke Zhu, Ken Goldberg^†, Linxi "Jim" Fan^†

International Conference on Machine Learning (ICML), 2026

Paper Project Page Code

We present CaP-X, a framework for evaluating coding agents on robot manipulation, with an agent harness for zero-shot control and reinforcement learning to improve code-based policies.

NitroGen: A Foundation Model for Generalist Gaming Agents

Loïc Magne*, Anas Awadalla*, Guanzhi Wang*^†, Yinzhen Xu, Joshua Belofsky, Fengyuan Hu, Joohwan Kim, Ludwig Schmidt, Georgia Gkioxari, Jan Kautz, Yisong Yue^†, Yejin Choi^†, Yuke Zhu^†, Linxi "Jim" Fan^†

Conference on Computer Vision and Pattern Recognition (CVPR), 2026

Best Paper Honorable Mention

Paper Project Page Code Model

We present NitroGen, a vision-action foundation model trained on 40,000 hours of gameplay videos across 1,000+ games that demonstrates strong cross-game generalization.

GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi "Jim" Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, Joel Jang, Zhenyu Jiang, Jan Kautz, Kaushil Kundalia, Lawrence Lao, Zhiqi Li, Zongyu Lin, Kevin Lin, Guilin Liu, Edith Llontop, Loic Magne, Ajay Mandlekar, Avnish Narayan, Soroush Nasiriany, Scott Reed, You Liang Tan, Guanzhi Wang, Zu Wang, Jing Wang, Qi Wang, Jiannan Xiang, Yuqi Xie, Yinzhen Xu, Zhenjia Xu, Seonghyeon Ye, Zhiding Yu, Ao Zhang, Hao Zhang, Yizhou Zhao, Ruijie Zheng, Yuke Zhu

arXiv preprint, 2025

Paper Project Page Code

We present GR00T N1, an open Vision-Language-Action foundation model for generalist humanoid robots.

Eureka: Human-Level Reward Design via Coding Large Language Models

Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi "Jim" Fan^†, Anima Anandkumar^†

International Conference on Learning Representations (ICLR), 2024

Paper Project Page Code

We present Eureka, an open-ended LLM-powered agent that designs reward functions for robot dexterity at super-human level.

Voyager: An Open-Ended Embodied Agent with Large Language Models

Guanzhi Wang, Yuqi Xie, Yunfan Jiang*, Ajay Mandlekar*, Chaowei Xiao, Yuke Zhu, Linxi "Jim" Fan^†, Anima Anandkumar^†

Transactions on Machine Learning Research (TMLR), 2024

Paper Project Page Code

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention.

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Linxi "Jim" Fan, Guanzhi Wang*, Yunfan Jiang*, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu^†, Anima Anandkumar^†

Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2022

Outstanding Paper Award

Paper Project Page Code

We introduce MineDojo, a new framework based on the popular Minecraft game for building generally capable, open-ended embodied agents.

FLARE: Robot Learning with Implicit World Modeling

Ruijie Zheng, Jing Wang, Scott Reed, Johan Bjorck, Yu Fang, Fengyuan Hu, Joel Jang, Kaushil Kundalia, Zongyu Lin, Loic Magne, Avnish Narayan, You Liang Tan, Guanzhi Wang, Qi Wang, Jiannan Xiang, Yinzhen Xu, Seonghyeon Ye, Jan Kautz, Furong Huang, Yuke Zhu, Linxi Fan

Conference on Robot Learning (CoRL), 2025

Paper Project Page

We introduce FLARE, a novel framework that integrates predictive latent world modeling into robot policy learning.

DreamGen: Unlocking Generalization in Robot Learning through Video World Models

Joel Jang, Seonghyeon Ye, Zongyu Lin, Jiannan Xiang, Johan Bjorck, Yu Fang, Fengyuan Hu, Spencer Huang, Kaushil Kundalia, Yen-Chen Lin, Loic Magne, Ajay Mandlekar, Avnish Narayan, You Liang Tan, Guanzhi Wang, Jing Wang, Qi Wang, Yinzhen Xu, Xiaohui Zeng, Kaiyuan Zheng, Ruijie Zheng, Ming-Yu Liu, Luke Zettlemoyer, Dieter Fox, Jan Kautz, Scott Reed, Yuke Zhu, Linxi Fan

Conference on Robot Learning (CoRL), 2025

Paper Project Page Code

We introduce DreamGen, a 4-stage pipeline for training robot policies that generalize across behaviors and environments through neural trajectories.

Strategist: Self-improvement of LLM Decision Making via Bi-Level Tree Search

Jonathan Light, Min Cai, Weiqin Chen, Guanzhi Wang, Xiusi Chen, Wei Cheng, Yisong Yue, Ziniu Hu

International Conference on Learning Representations (ICLR), 2025

Paper Project Page

We propose Strategist, a bi-level framework that facilitates high-level abstract strategy learning with LLMs.

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Open X-Embodiment Collaboration (293+ authors including Guanzhi Wang)

International Conference on Robotics and Automation (ICRA), 2024

Paper Project Page Code

We present Open X-Embodiment, the largest open-source robot dataset with 1M+ trajectories from 22 embodiments.

VIMA: General Robot Manipulation with Multimodal Prompts

Yunfan Jiang, Agrim Gupta*, Zichen "Charles" Zhang*, Guanzhi Wang*, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu^†, Linxi "Jim" Fan^†

International Conference on Machine Learning (ICML), 2023

Paper Project Page Code

We introduce a novel multimodal prompting formulation that converts diverse robot manipulation tasks into a uniform sequence modeling problem.

SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies

Linxi "Jim" Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Anima Anandkumar

International Conference on Machine Learning (ICML), 2021

Paper Project Page Code

We propose SECANT, a novel self-expert cloning technique that leverages image augmentation in two stages to decouple robust representation learning from policy optimization.

iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes

Bokui Shen*, Fei Xia*, Chengshu Li*, Roberto Martín-Martín*, Linxi "Jim" Fan, Guanzhi Wang, Claudia Pérez-D'Arpino, Shyamal Buch, Sanjana Srivastava, Lyne P. Tchapmi, Micael E. Tchapmi, Kent Vainio, Josiah Wong, Li Fei-Fei, Silvio Savarese

International Conference on Intelligent Robots and Systems (IROS), 2021

Paper Project Page Code

We present iGibson, a novel simulation environment for developing interactive robotic agents in large-scale realistic scenes.

Deep Video Matting via Spatio-Temporal Alignment and Aggregation

Yanan Sun, Guanzhi Wang*, Qiao Gu*, Chi-Keung Tang, Yu-Wing Tai

Conference on Computer Vision and Pattern Recognition (CVPR), 2021

Paper Code Dataset

We propose a deep learning-based video matting framework which employs a novel and effective spatio-temporal feature aggregation module.

RubiksNet: Learnable 3D-Shift for Efficient Video Action Recognition

Linxi "Jim" Fan*, Shyamal Buch*, Guanzhi Wang, Ryan Cao, Yuke Zhu, Juan Carlos Niebles, Li Fei-Fei

European Conference on Computer Vision (ECCV), 2020

Paper Project Page Video Code

We propose RubiksNet, a new efficient architecture for video action recognition based on a proposed learnable 3D spatiotemporal shift operation (RubiksShift).

LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup

Qiao Gu*, Guanzhi Wang*, Mang Tik Chiu, Yu-Wing Tai, Chi-Keung Tang

International Conference on Computer Vision (ICCV), 2019

Paper Project Page Code Dataset

We propose a local adversarial disentangling network for facial makeup and de-makeup, using multiple and overlapping local discriminators in a content-style disentangling network.