8 citations · 30 across the 20 of their papers we have counts for
20 papers
4D Visual Pre-training for Robot Learning
Chengkai Hou, Yanjie Ze, Yankai Fu +5
General visual representations learned from web-scale datasets for robotics have achieved great success in recent years, enabling data-efficient robot learning on manipulation task…
A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Zilin Kang, Chenyuan Hu, Yu Luo +3
Deep reinforcement learning for continuous control has recently achieved impressive progress. However, existing methods often suffer from primacy bias, a tendency to overfit early…
DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove
Han Zhang, Songbo Hu, Zhecheng Yuan +1
Dexterous hand teleoperation plays a pivotal role in enabling robots to achieve human-level manipulation dexterity. However, current teleoperation systems often rely on expensive e…
DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo
Junzhe Zhu, Yuanchen Ju, Junyi Zhang +4
Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Co…
Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets
Guangqi Jiang, Yifei Sun, Tao Huang +3
The pre-training of visual representations has enhanced the efficiency of robot learning. Due to the lack of large-scale in-domain robotic datasets, prior works utilize in-the-wild…
Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning
Zhecheng Yuan, Tianming Wei, Shuiqi Cheng +3
Can we endow visuomotor robots with generalization capabilities to operate in diverse open-world scenarios? In this paper, we propose \textbf{Maniwhere}, a generalizable framework…
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…
World Models with Hints of Large Language Models for Goal Achieving
Zeyuan Liu, Ziyu Huan, Xiyao Wang +5
Reinforcement learning struggles in the face of long-horizon tasks and sparse goals due to the difficulty in manual reward specification. While existing methods address this by add…
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…
RoboDuet: Learning a Cooperative Policy for Whole-body Legged Loco-Manipulation
Guoping Pan, Qingwei Ben, Zhecheng Yuan +6
Fully leveraging the loco-manipulation capabilities of a quadruped robot equipped with a robotic arm is non-trivial, as it requires controlling all degrees of freedom (DoFs) of the…
RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation
Junting Chen, Yao Mu, Qiaojun Yu +11
Rapid progress in high-level task planning and code generation for open-world robot manipulation has been witnessed in Embodied AI. However, previous studies put much effort into g…
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
Tianying Ji, Yongyuan Liang, Yan Zeng +7
The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explo…
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, Xiyao Wang +7
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks. Premier-T…
Generalizable Visual Reinforcement Learning with Segment Anything Model
Ziyu Wang, Yanjie Ze, Yifei Sun +2
Learning policies that can generalize to unseen environments is a fundamental challenge in visual reinforcement learning (RL). While most current methods focus on acquiring robust…
Diffusion Reward: Learning Rewards via Conditional Video Diffusion
Tao Huang, Guangqi Jiang, Yanjie Ze +1
Learning rewards from expert videos offers an affordable and effective solution to specify the intended behaviors for reinforcement learning (RL) tasks. In this work, we propose Di…
H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation
Yanjie Ze, Yuyao Liu, Ruizhe Shi +4
Human hands possess remarkable dexterity and have long served as a source of inspiration for robotic manipulation. In this work, we propose a human andfor…
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
Ruizhe Shi, Yuyao Liu, Yanjie Ze +2
Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets. In real-world scenarios, data collection could be costly and risky; therefore,…
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
Guowei Xu, Ruijie Zheng, Yongyuan Liang +12
Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the…
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
Xiyao Wang, Ruijie Zheng, Yanchao Sun +4
Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration using current policy for d…
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Lirui Wang, Yiyang Ling, Zhecheng Yuan +6
Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data. However, exi…