4 citations · 13 across the 13 of their papers we have counts for
13 papers
AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
Jiawei Zhang, Kaizhe Hu, Yingqian Huang +3
Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity.…
OmniClone: Engineering a Robust, All-Rounder Whole-Body Humanoid Teleoperation System
Yixuan Li, Le Ma, Yutang Lin +8
Whole-body humanoid teleoperation enables humans to remotely control humanoid robots, serving as both a real-time operational tool and a scalable engine for collecting demonstratio…
Failure-Aware RL: Reliable Offline-to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation
Huanyu Li, Kun Lei, Sheng Zang +5
Post-training algorithms based on deep reinforcement learning can push the limits of robotic models for specific objectives, such as generalizability, accuracy, and robustness. How…
Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids
Kaizhe Hu, Haochen Shi, Yao He +3
Simulation-based reinforcement learning (RL) has significantly advanced humanoid locomotion tasks, yet direct real-world RL from scratch or adapting from pretrained policies remain…
Stem-OB: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversion
Kaizhe Hu, Zihang Rui, Yao He +3
Visual imitation learning methods demonstrate strong performance, yet they lack generalization when faced with visual input perturbations, including variations in lighting and text…
Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion
Yongyuan Liang, Tingqiang Xu, Kaizhe Hu +3
Can we generate a control policy for an agent using just one demonstration of desired behaviors as a prompt, as effortlessly as creating an image from a textual description? In thi…
Rethinking Transformers in Solving POMDPs
Chenhao Lu, Ruizhe Shi, Yuyao Liu +3
Sequential decision-making algorithms such as reinforcement learning (RL) in real-world scenarios inevitably face environments with partial observability. This paper scrutinizes th…
Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation
Yuanchen Ju, Kaizhe Hu, Guowei Zhang +3
Enabling robotic manipulation that generalizes to out-of-distribution scenes is a crucial step toward open-world embodied intelligence. For human beings, this ability is rooted in…
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
Kun Lei, Zhengmao He, Chenhao Lu +3
Combining offline and online reinforcement learning (RL) is crucial for efficient and safe learning. However, previous approaches treat offline and online learning as separate proc…
Can Pre-Trained Text-to-Image Models Generate Visual Goals for Reinforcement Learning?
Jialu Gao, Kaizhe Hu, Guowei Xu +1
Pre-trained text-to-image generative models can produce diverse, semantically rich, and realistic images from natural language descriptions. Compared with language, images usually…
RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
Zhecheng Yuan, Sizhe Yang, Pu Hua +3
Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization…
Decision Transformer under Random Frame Dropping
Kaizhe Hu, Ray Chen Zheng, Yang Gao +1
Controlling agents remotely with deep reinforcement learning~(DRL) in the real world is yet to come. One crucial stepping stone is to devise RL algorithms that are robust in the fa…
Extraneousness-Aware Imitation Learning
Ray Chen Zheng, Kaizhe Hu, Zhecheng Yuan +2
Visual imitation learning provides an effective framework to learn skills from demonstrations. However, the quality of the provided demonstrations usually significantly affects the…