3 citations · 11 across the 16 of their papers we have counts for
17 papers
UniTac2Pose: A Unified Approach Learned in Simulation for Category-level Visuotactile In-hand Pose Estimation
Mingdong Wu, Long Yang, Jin Liu +5
Accurate estimation of the in-hand pose of an object based on its CAD model is crucial in both industrial applications and everyday tasks, ranging from positioning workpieces and a…
SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training
Mingdong Wu, Lehong Wu, Yizhuo Wu +9
Autonomous learning of dexterous, long-horizon robotic skills has been a longstanding pursuit of embodied AI. Recent advances in robotic reinforcement learning (RL) have demonstrat…
Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation
Jinzhou Li, Tianhao Wu, Jiyao Zhang +6
Effectively utilizing multi-sensory data is important for robots to generalize across diverse tasks. However, the heterogeneous nature of these modalities makes fusion challenging.…
Boosting Universal LLM Reward Design through Heuristic Reward Observation Space Evolution
Zen Kit Heng, Zimeng Zhao, Tianhao Wu +4
Large Language Models (LLMs) are emerging as promising tools for automated reinforcement learning (RL) reward design, owing to their robust capabilities in commonsense reasoning an…
AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning
Yuanfei Wang, Xiaojie Zhang, Ruihai Wu +6
Articulated object manipulation is a critical capability for robots to perform various tasks in real-world scenarios. Composed of multiple parts connected by joints, articulated ob…
CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World
Yankai Fu, Qiuxuan Feng, Ning Chen +8
Achieving human-level dexterity in robots is a key objective in the field of robotic manipulation. Recent advancements in 3D-based imitation learning have shown promising results,…
MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-object Demand-driven Navigation
Hongcheng Wang, Peiqi Liu, Wenzhe Cai +3
The process of satisfying daily demands is a fundamental aspect of humans' daily lives. With the advancement of embodied AI, robots are increasingly capable of satisfying human dem…
Canonical Representation and Force-Based Pretraining of 3D Tactile for Dexterous Visuo-Tactile Policy Learning
Tianhao Wu, Jinzhou Li, Jiyao Zhang +2
Tactile sensing plays a vital role in enabling robots to perform fine-grained, contact-rich tasks. However, the high dimensionality of tactile data, due to the large coverage on de…
GFPack++: Attention-Driven Gradient Fields for Optimizing 2D Irregular Packing
Tianyang Xue, Lin Lu, Yang Liu +5
2D irregular packing is a classic combinatorial optimization problem with various applications, such as material utilization and texture atlas generation. Due to its NP-hard nature…
Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking
Jiyao Zhang, Weiyao Huang, Bo Peng +5
6D Object Pose Estimation is a crucial yet challenging task in computer vision, suffering from a significant lack of large-scale datasets. This scarcity impedes comprehensive evalu…
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning
Qian Long, Fangwei Zhong, Mingdong Wu +2
Multi-agent systems (MAS) need to adaptively cope with dynamic environments, changing agent populations, and diverse tasks. However, most of the multi-agent systems cannot easily h…
Dexterous Functional Pre-Grasp Manipulation with Diffusion Policy
Tianhao Wu, Yunchong Gan, Mingdong Wu +4
In real-world scenarios, objects often require repositioning and reorientation before they can be grasped, a process known as pre-grasp manipulation. Learning universal dexterous f…
LVDiffusor: Distilling Functional Rearrangement Priors from Large Models into Diffusor
Yiming Zeng, Mingdong Wu, Long Yang +4
Object rearrangement, a fundamental challenge in robotics, demands versatile strategies to handle diverse objects, configurations, and functional needs. To achieve this, the AI rob…
Learning Gradient Fields for Scalable and Generalizable Irregular Packing
Tianyang Xue, Mingdong Wu, Lin Lu +3
The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly…
Score-PA: Score-based 3D Part Assembly
Junfeng Cheng, Mingdong Wu, Ruiyuan Zhang +3
Autonomous 3D part assembly is a challenging task in the areas of robotics and 3D computer vision. This task aims to assemble individual components into a complete shape without re…
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
Hongcheng Wang, Andy Guan Hong Chen, Xiaoqi Li +2
The task of Visual Object Navigation (VON) involves an agent's ability to locate a particular object within a given scene. In order to successfully accomplish the VON task, two ess…
GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
Tianhao Wu, Mingdong Wu, Jiyao Zhang +2
The use of anthropomorphic robotic hands for assisting individuals in situations where human hands may be unavailable or unsuitable has gained significant importance. In this paper…