10 citations · 32 across the 12 of their papers we have counts for
12 papers
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…
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…