2 citations · 3 across the 11 of their papers we have counts for
12 papers
SimEX: Simulation-Integrated Robotics AutoResearch
Jiaheng Hu, Roberto Martin-Martin, Peter Stone +3
Coding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital world. However, bringing this su…
VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids
Zichao Hu, Zifan Xu, Dongsik Chang +6
The ability to push large objects in a goal-directed manner using onboard egocentric perception is an essential skill for humanoid robots to perform complex tasks such as material…
Factored Latent Action World Models
Zizhao Wang, Chang Shi, Jiaheng Hu +4
Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions provide a natural interface for u…
Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input
Zifan Xu, Myoungkyu Seo, Dongmyeong Lee +8
Learning fast and robust ball-kicking skills is a critical capability for humanoid soccer robots, yet it remains a challenging problem due to the need for rapid leg swings, postura…
RoboSSM: Scalable In-context Imitation Learning via State-Space Models
Youngju Yoo, Jiaheng Hu, Yifeng Zhu +4
In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations. By eliminating the need for parameter updates at dep…
SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training
Jiaheng Hu, Peter Stone, Roberto Martín-Martín
Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators. While reinforcement…
Learning to Look: Seeking Information for Decision Making via Policy Factorization
Shivin Dass, Jiaheng Hu, Ben Abbatematteo +2
Many robot manipulation tasks require active or interactive exploration behavior in order to be performed successfully. Such tasks are ubiquitous in embodied domains, where agents…
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
Zizhao Wang, Jiaheng Hu, Caleb Chuck +5
Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free environment interaction. Existing unsupervised…
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
Jiaheng Hu, Zizhao Wang, Peter Stone +1
A hallmark of intelligent agents is the ability to learn reusable skills purely from unsupervised interaction with the environment. However, existing unsupervised skill discovery m…
FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning
Jiaheng Hu, Rose Hendrix, Ali Farhadi +5
In recent years, the Robotics field has initiated several efforts toward building generalist robot policies through large-scale multi-task Behavior Cloning. However, direct deploym…
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
Chen Tang, Ben Abbatematteo, Jiaheng Hu +3
Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications,…
TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
Shivin Dass, Wensi Ai, Yuqian Jiang +6
A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder tha…