Online Implicit Surfaces for Obstacle Modeling and Avoidance
Keywords: Deformable object manipulation, contact, model predictive control, online learning
TL;DR: Using Gaussian processes to model obstacles online, thereby enabling manipulation tasks.
Abstract: Adapting model-based control to novel environ-
ments is challenging as dynamics models learned offline may not
generalize to the obstacle configuration of the novel environment.
We propose a method to identify and avoid obstacles online
whose geometry is not known a priori without updating the
offline, nominal, dynamics. Our method relies on a Gaussian pro-
cess implicit surface (GPIS) to construct data-efficient obstacle
representations using visual and inferrred contact data derived
from observed states and dynamics predictions. This allows us to
design a model predictive controller (MPC) using the uncertainty
estimates provided by the GPIS to successfully navigate around
obstacles to complete multiple manipulation tasks. By modeling
the environment instead of directly adapting the dynamics, our
method is able to solve both low-dimensional peg-in-hole tasks
and high-dimensional rope and cable manipulation tasks. This
enables our method to succeed in 30/30 trials vs 15/30 for a
baseline on a simulated rope manipulation task while requiring
63% fewer control steps to succeed.
Submission Number: 15
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