Learning Robot Geometry as Distance Fields: Applications to Whole-body Manipulation

by   Yiming Li, et al.
Idiap Research Institute

In this work, we propose to learn robot geometry as distance fields (RDF), which extend the signed distance field (SDF) of the robot with joint configurations. Unlike existing methods that learn an implicit representation encoding joint space and Euclidean space together, the proposed RDF approach leverages the kinematic chain of the robot, which reduces the dimensionality and complexity of the problem, resulting in more accurate and reliable SDFs. A simple and flexible approach that exploits basis functions to represent SDFs for individual robot links is presented, providing a smoother representation and improved efficiency compared to neural networks. RDF is naturally continuous and differentiable, enabling its direct integration as cost functions in robot tasks. It also allows us to obtain high-precision robot surface points with any desired spatial resolution, with the capability of whole-body manipulation. We verify the effectiveness of our RDF representation by conducting various experiments in both simulations and with the 7-axis Franka Emika robot. We compare our approach against baseline methods and demonstrate its efficiency in dual-arm settings for tasks involving collision avoidance and whole-body manipulation. Project page: https://sites.google.com/view/lrdf/homehttps://sites.google.com/view/lrdf/home


page 2

page 7

page 8


Neural Grasp Distance Fields for Robot Manipulation

We formulate grasp learning as a neural field and present Neural Grasp D...

Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning

Action representation is an important yet often overlooked aspect in end...

Fast Joint Space Model-Predictive Control for Reactive Manipulation

Sampling-based model predictive control (MPC) is a promising tool for fe...

Learning from Demonstration with Weakly Supervised Disentanglement

Robotic manipulation tasks, such as wiping with a soft sponge, require c...

VisuoSpatial Foresight for Physical Sequential Fabric Manipulation

Robotic fabric manipulation has applications in home robotics, textiles,...

Theseus: A Library for Differentiable Nonlinear Optimization

We present Theseus, an efficient application-agnostic open source librar...

Please sign up or login with your details

Forgot password? Click here to reset