Sampling-Based Model Predictive Control for Contact-Rich Manipulation

Sharanya Venkatesh, Bibit Bianchini, Alp Aydinoglu, Michael Posa

Keywords: dexterous manipulation, sampling and multi-contact MPC, contact-rich manipulation.

Abstract:

For robotic systems to be useful in everyday lives,
they need to perform dexterous manipulation tasks in messy
human environments. Enumerating all the possible physical
interactions a robot can make with its environment and other
objects is intractable, necessitating ways for robots to resolve
how to perform tasks online, and in real time. However, on-
the-fly globally optimal control is infeasible due to the high
required computational load. Workarounds that preempt the
computational cost by training or computing in advance lack
the necessary ability to recover when real events are not
foreseen by previous data or offline plans. Recent advances
in model predictive control (MPC) leverage significant model
simplification but demonstrate contact-implicit controllers ca-
pable of real time rates. These local model-based controllers
require extra assistance for even simple manipulation tasks. We
demonstrate using parallel local model-based control methods,
ultimately making hybrid decisions at two levels: a contact-
implicit locally optimal controller, wrapped by a layer which
considers the controller’s performance from a sampling of end
effector configurations. Our hierarchical controller is shown to
perform simulated rolling tasks with a sphere and a jack at
rates with expected real-time capability.

Submission Number: 19