2 citations · 3 across the 13 of their papers we have counts for
13 papers · 1 filter
Robot-GST: geometry-aware spatial-temporal robot policy representation and evaluation
Sichao Liu, Zekun Wang, Lixuan Tang +6
Robotic manipulation policies are advancing rapidly with increasing reliance on vision-language models for end-to-end decision making. However, reliable deployment remains challeng…
Spline Policy: A Structured Representation for Robot Policies
Mengze Tian, Yiming Li, Sichao Liu +2
Modern imitation-learning policies for robot manipulation often represent actions as fixed-resolution action chunks, which are simple and effective but expose limited geometric and…
Physics-Informed Eikonal Caging for Whole-Arm Manipulation Planning
Yan Zhang, Yiming Li, Yifei Dong +2
Planning contact-rich whole-arm manipulation is challenging because interactions that involve extended robot geometry give rise to complex contact dynamics that are difficult to mo…
Planning-aligned Token Compression for Long-Context Autonomous Driving
Zhixuan Liang, Yuxiao Chen, Yurong You +12
Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly exceed real-time computationa…
Ergodic Imitation for Adaptive Exploration around Demonstrations
Ziyi Xu, Cem Bilaloglu, Yiming Li +1
In robotics, a common challenge in imitation learning is the mismatch between training and deployment conditions, caused, for example, by environmental changes or imperfect observa…
Fast and Safe Trajectory Optimization for Mobile Manipulators With Neural Configuration Space Distance Field
Yulin Li, Zhiyuan Song, Yiming Li +8
Mobile manipulators promise agile, long-horizon behavior by coordinating base and arm motion, yet whole-body trajectory optimization in cluttered, confined spaces remains difficult…
Geometry-aware Policy Imitation
Yiming Li, Nael Darwiche, Amirreza Razmjoo +4
We propose a Geometry-aware Policy Imitation (GPI) approach that rethinks imitation learning by treating demonstrations as geometric curves rather than collections of state-action…
From Movement Primitives to Distance Fields to Dynamical Systems
Yiming Li, Sylvain Calinon
Developing autonomous robots capable of learning and reproducing complex motions from demonstrations remains a fundamental challenge in robotics. On the one hand, movement primitiv…
Safe Dynamic Motion Generation in Configuration Space Using Differentiable Distance Fields
Xuemin Chi, Yiming Li, Jihao Huang +3
Generating collision-free motions in dynamic environments is a challenging problem for high-dimensional robotics, particularly under real-time constraints. Control Barrier Function…
A Riemannian Take on Distance Fields and Geodesic Flows in Robotics
Yiming Li, Jiacheng Qiu, Sylvain Calinon
Distance functions are crucial in robotics for representing spatial relationships between a robot and its environment. They provide an implicit, continuous, and differentiable repr…
Configuration Space Distance Fields for Manipulation Planning
Yiming Li, Xuemin Chi, Amirreza Razmjoo +1
The signed distance field is a popular implicit shape representation in robotics, providing geometric information about objects and obstacles in a form that can easily be combined…
Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials
Ante Marić, Yiming Li, Sylvain Calinon
Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representatio…
Geometric Projectors: Geometric Constraints based Optimization for Robot Behaviors
Xuemin Chi, Tobias Löw, Yiming Li +2
Generating motion for robots that interact with objects of various shapes is a complex challenge, further complicated when the robot's own geometry and multiple desired behaviors a…