204 citations · 447 across the 16 of their papers we have counts for
16 papers
GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
Kaiyuan Chen, Shuangyu Xie, Letian Fu +21
For robots to work reliably in commercial and industrial applications, can recent advances in agentic coding systems combine interpretable robot programming with the open-world ada…
ASPIRE: Agentic /Skills Discovery for Robotics
Runyu Lu, Yubo Wu, Ethan Kou +13
Traditional robot programming is notoriously challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse environment conf…
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
Wenli Xiao, Jia Xie, Tonghe Zhang +14
Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of gener…
CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
Letian Fu, Justin Yu, Karim El-Refai +13
"Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as autonomous controllers for embodied ma…
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
Joel Jang, Seonghyeon Ye, Zongyu Lin +25
We introduce DreamGen, a simple yet highly effective 4-stage pipeline for training robot policies that generalize across behaviors and environments through neural trajectories - sy…
FLARE: Robot Learning with Implicit World Modeling
Ruijie Zheng, Jing Wang, Scott Reed +18
We introduce uture tent presentation Alignment (), a novel framework that integrates predictive latent world modeling into rob…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
NVIDIA, :, Johan Bjorck +40
General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist…
Eureka: Human-Level Reward Design via Coding Large Language Models
Yecheng Jason Ma, William Liang, Guanzhi Wang +6
Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation ta…
Voyager: An Open-Ended Embodied Agent with Large Language Models
Guanzhi Wang, Yuqi Xie, Yunfan Jiang +5
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…
VIMA: General Robot Manipulation with Multimodal Prompts
Yunfan Jiang, Agrim Gupta, Zichen Zhang +7
Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task speci…
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang +7
Autonomous agents have made great strides in specialist domains like Atari games and Go. However, they typically learn tabula rasa in isolated environments with limited and manuall…
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
Linxi Fan, Guanzhi Wang, De-An Huang +4
Generalization has been a long-standing challenge for reinforcement learning (RL). Visual RL, in particular, can be easily distracted by irrelevant factors in high-dimensional obse…
Deep Video Matting via Spatio-Temporal Alignment and Aggregation
Yanan Sun, Guanzhi Wang, Qiao Gu +2
Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent…
iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes
Bokui Shen, Fei Xia, Chengshu Li +12
We present iGibson 1.0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interac…
LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
Qiao Gu, Guanzhi Wang, Mang Tik Chiu +2
We propose a local adversarial disentangling network (LADN) for facial makeup and de-makeup. Central to our method are multiple and overlapping local adversarial discriminators in…