Representing Robot Geometry as Distance Fields: Applications to Whole-body Manipulation
arxiv(2023)
摘要
In this work, we propose a novel approach to represent robot geometry as
distance fields (RDF) that extends the principle of signed distance fields
(SDFs) to articulated kinematic chains. Our method employs a combination of
Bernstein polynomials to encode the signed distance for each robot link with
high accuracy and efficiency while ensuring the mathematical continuity and
differentiability of SDFs. We further leverage the kinematics chain of the
robot to produce the SDF representation in joint space, allowing robust
distance queries in arbitrary joint configurations. The proposed RDF
representation is differentiable and smooth in both task and joint spaces,
enabling its direct integration to optimization problems. Additionally, the
0-level set of the robot corresponds to the robot surface, which can be
seamlessly integrated into whole-body manipulation tasks. We conduct various
experiments in both simulations and with 7-axis Franka Emika robots, comparing
against baseline methods, and demonstrating its effectiveness in collision
avoidance and whole-body manipulation tasks. Project page:
https://sites.google.com/view/lrdf/home
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要