4 citations · 5 across the 22 of their papers we have counts for
23 papers
VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness
Hang Li, Mingxin Zhang, Zihan Wu +9
Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rely on constrained camera view…
Knowledge Reutilization in Meta-Reinforcement Learning
Yuan Meng, Bo Wang, Juan de los Rios Ruiz +4
Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-s…
Dreaming the Unseen: World Model-regularized Diffusion Policy for Out-of-Distribution Robustness
Ziou Hu, Xiangtong Yao, Yuan Meng +2
Diffusion policies excel at visuomotor control but often fail catastrophically under severe out-of-distribution (OOD) disturbances, such as unexpected object displacements or visua…
USIS-PGM: Photometric Gaussian Mixtures for Underwater Salient Instance Segmentation
Lin Hong, Xiangtong Yao, Mürüvvet Bozkurt +2
Underwater salient instance segmentation (USIS) is crucial for marine robotic systems, as it enables both underwater salient object detection and instance-level mask prediction for…
AffordanceGrasp-R1:Leveraging Reasoning-Based Affordance Segmentation with Reinforcement Learning for Robotic Grasping
Dingyi Zhou, Mu He, Zhuowei Fang +4
We introduce AffordanceGrasp-R1, a reasoning-driven affordance segmentation framework for robotic grasping that combines a chain-of-thought (CoT) cold-start strategy with reinforce…
Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulation
Zebin Jiang, Tianle Jin, Xiangtong Yao +2
Grasping is one of the most fundamental challenging capabilities in robotic manipulation, especially in unstructured, cluttered, and semantically diverse environments. Recent resea…
Inference-stage Adaptation-projection Strategy Adapts Diffusion Policy to Cross-manipulators Scenarios
Xiangtong Yao, Yirui Zhou, Yuan Meng +7
Diffusion policies are powerful visuomotor models for robotic manipulation, yet they often fail to generalize to manipulators or end-effectors unseen during training and struggle t…
UOPSL: Unpaired OCT Predilection Sites Learning for Fundus Image Diagnosis Augmentation
Zhihao Zhao, Yinzheng Zhao, Junjie Yang +6
Significant advancements in AI-driven multimodal medical image diagnosis have led to substantial improvements in ophthalmic disease identification in recent years. However, acquiri…
CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging
Zhihao Zhao, Yinzheng Zhao, Junjie Yang +6
Recent advancements in foundation models, such as the Segment Anything Model (SAM), have significantly impacted medical image segmentation, especially in retinal imaging, where pre…
Whisker-based Active Tactile Perception for Contour Reconstruction
Yixuan Dang, Qinyang Xu, Yu Zhang +5
Perception using whisker-inspired tactile sensors currently faces a major challenge: the lack of active control in robots based on direct contact information from the whisker. To a…
Robotic Long-Horizon Manipulation with Bayesian Non-parametric Skill Priors
Yuan Meng, Xiangtong Yao, Kejia Chen +5
Long-horizon manipulation under sparse rewards remains challenging for reinforcement learning due to delayed feedback and inefficient exploration. Existing skill-based approaches o…
Predicting the Road Ahead: A Knowledge Graph based Foundation Model for Scene Understanding in Autonomous Driving
Hongkuan Zhou, Stefan Schmid, Yicong Li +3
The autonomous driving field has seen remarkable advancements in various topics, such as object recognition, trajectory prediction, and motion planning. However, current approaches…
Robotic Long-Horizon Manipulation with Progressive In-Context Code Generation and Episodic Feedback
Yuan Meng, Xiangtong Yao, Haihui Ye +6
Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-conditioned frameworks often rely…
Pick-and-place Manipulation Across Grippers Without Retraining: A Learning-optimization Diffusion Policy Approach
Xiangtong Yao, Yirui Zhou, Yuan Meng +7
Current robotic pick-and-place policies typically require consistent gripper configurations across training and inference. This constraint imposes high retraining or fine-tuning co…
Locomotion Generation for a Rat Robot based on Environmental Changes via Reinforcement Learning
Xinhui Shan, Yuhong Huang, Zhenshan Bing +4
This research focuses on developing reinforcement learning approaches for the locomotion generation of small-size quadruped robots. The rat robot NeRmo is employed as the experimen…
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
Yu Zhang, Long Wen, Xiangtong Yao +4
This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consi…
Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments
Yu Zhang, Guangyao Tian, Long Wen +5
This paper proposes a LiDAR-based goal-seeking and exploration framework, addressing the efficiency of online obstacle avoidance in unstructured environments populated with static…
Bridging Language and Action: A Survey of Language-Conditioned Robot Manipulation
Xiangtong Yao, Hongkuan Zhou, Oier Mees +12
Language-conditioned robot manipulation is an emerging field aimed at enabling seamless communication and cooperation between humans and robotic agents by teaching robots to compre…
Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data
Hongkuan Zhou, Zhenshan Bing, Xiangtong Yao +4
The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots t…
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
Mingyang Wang, Zhenshan Bing, Xiangtong Yao +5
Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing…
Safety Guaranteed Manipulation Based on Reinforcement Learning Planner and Model Predictive Control Actor
Zhenshan Bing, Aleksandr Mavrichev, Sicong Shen +4
Deep reinforcement learning (RL) has been endowed with high expectations in tackling challenging manipulation tasks in an autonomous and self-directed fashion. Despite the signific…
Meta-Reinforcement Learning via Language Instructions
Zhenshan Bing, Alexander Koch, Xiangtong Yao +2
Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamenta…
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions
Xiangtong Yao, Zhenshan Bing, Genghang Zhuang +4
Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multi-…