7 papers
GIFT: Geometry-Induced Functional Transfer for Category-level Object Manipulation
Cristiana de Farias, Luis Figueredo, Riddhiman Laha +5
Robotic manipulation of unfamiliar objects in new environments is challenging due to limited generalisation capabilities. We propose a new skill transfer framework, GIFT (Geometry-…
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…
UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning
Zewen Yang, Xiaobing Dai, Dian Yu +4
Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existi…
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-…
Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems
Shreenabh Agrawal, Hugo T. M. Kussaba, Lingyun Chen +4
Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One es…
On the Synthesis of Reactive Collision-Free Whole-Body Robot Motions: A Complementarity-based Approach
Haowen Yao, Riddhiman Laha, Anirban Sinha +4
This paper is about generating motion plans for high degree-of-freedom systems that account for collisions along the entire body. A particular class of mathematical programs with c…