51 citations · 96 across the 19 of their papers we have counts for
19 papers
From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation
Ju Dong, Liding Zhang, Lei Zhang +7
Generative policies based on diffusion and flow matching achieve strong performance in robotic manipulation by modeling multi-modal human demonstrations. However, their reliance on…
Just in time Informed Trees: Manipulability-Aware Asymptotically Optimized Motion Planning
Kuanqi Cai, Liding Zhang, Xinwen Su +6
In high-dimensional robotic path planning, traditional sampling-based methods often struggle to efficiently identify both feasible and optimal paths in complex, multi-obstacle envi…
TacUMI: A Multi-Modal Universal Manipulation Interface for Contact-Rich Tasks
Tailai Cheng, Kejia Chen, Lingyun Chen +8
Task decomposition is critical for understanding and learning complex long-horizon manipulation tasks. Especially for tasks involving rich physical interactions, relying solely on…
M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation
Ju Dong, Lei Zhang, Liding Zhang +8
Mobile manipulation requires the coordinated control of a mobile base and a robotic arm while simultaneously perceiving both global scene context and fine-grained object details. E…
Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler
Liding Zhang, Kuanqi Cai, Yu Zhang +5
Path planning in robotics often involves solving continuously valued, high-dimensional problems. Popular informed approaches include graph-based searches, such as A*, and sampling-…
Language-Enhanced Mobile Manipulation for Efficient Object Search in Indoor Environments
Liding Zhang, Zeqi Li, Kuanqi Cai +3
Enabling robots to efficiently search for and identify objects in complex, unstructured environments is critical for diverse applications ranging from household assistance to indus…
Deep Fuzzy Optimization for Batch-Size and Nearest Neighbors in Optimal Robot Motion Planning
Liding Zhang, Qiyang Zong, Yu Zhang +2
Efficient motion planning algorithms are essential in robotics. Optimizing essential parameters, such as batch size and nearest neighbor selection in sampling-based methods, can en…
Genetic Informed Trees (GIT*): Path Planning via Reinforced Genetic Programming Heuristics
Liding Zhang, Kuanqi Cai, Zhenshan Bing +2
Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective. This process relies on heuristic functions to guide the sea…
APT*: Asymptotically Optimal Motion Planning via Adaptively Prolated Elliptical R-Nearest Neighbors
Liding Zhang, Sicheng Wang, Kuanqi Cai +5
Optimal path planning aims to determine a sequence of states from a start to a goal while accounting for planning objectives. Popular methods often integrate fixed batch sizes and…
Tree-Based Grafting Approach for Bidirectional Motion Planning with Local Subsets Optimization
Liding Zhang, Yao Ling, Zhenshan Bing +3
Bidirectional motion planning often reduces planning time compared to its unidirectional counterparts. It requires connecting the forward and reverse search trees to form a continu…
Elliptical K-Nearest Neighbors -- Path Optimization via Coulomb's Law and Invalid Vertices in C-space Obstacles
Liding Zhang, Zhenshan Bing, Yu Zhang +5
Path planning has long been an important and active research area in robotics. To address challenges in high-dimensional motion planning, this study introduces the Force Direction…
Direction Informed Trees (DIT*): Optimal Path Planning via Direction Filter and Direction Cost Heuristic
Liding Zhang, Kejia Chen, Kuanqi Cai +7
Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Info…
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…
Motion Planning for Robotics: A Review for Sampling-based Planners
Liding Zhang, Kuanqi Cai, Zewei Sun +5
Recent advancements in robotics have transformed industries such as manufacturing, logistics, surgery, and planetary exploration. A key challenge is developing efficient motion pla…
TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation
Yansong Wu, Zongxie Chen, Fan Wu +6
Assembly is a crucial skill for robots in both modern manufacturing and service robotics. However, mastering transferable insertion skills that can handle a variety of high-precisi…
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…
Real-time Contact State Estimation in Shape Control of Deformable Linear Objects under Small Environmental Constraints
Kejia Chen, Zhenshan Bing, Yansong Wu +4
Controlling the shape of deformable linear objects using robots and constraints provided by environmental fixtures has diverse industrial applications. In order to establish robust…
Flexible Informed Trees (FIT*): Adaptive Batch-Size Approach in Informed Sampling-Based Path Planning
Liding Zhang, Zhenshan Bing, Kejia Chen +8
In path planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sampling-based planners. A notable example is Batch Informed Trees (BIT*), where p…