LVIS: Learning from Value Function Intervals for Contact-Aware Robot Controllers

TL;DR

LVIS introduces a novel approach for contact-aware robot control by learning value function intervals through partial mixed-integer optimization, enabling effective policy training and control in contact-rich environments.

Contribution

It proposes a new method that combines global mixed-integer optimization with value function learning to address local minima and non-uniqueness in contact-rich control tasks.

Findings

Successfully applied to cart-pole with walls

Effective on planar humanoid robot model

Demonstrates control through contact

Abstract

Guided policy search is a popular approach for training controllers for high-dimensional systems, but it has a number of pitfalls. Non-convex trajectory optimization has local minima, and non-uniqueness in the optimal policy itself can mean that independently-optimized samples do not describe a coherent policy from which to train. We introduce LVIS, which circumvents the issue of local minima through global mixed-integer optimization and the issue of non-uniqueness through learning the optimal value function (or cost-to-go) rather than the optimal policy. To avoid the expense of solving the mixed-integer programs to full global optimality, we instead solve them only partially, extracting intervals containing the true cost-to-go from early termination of the branch-and-bound algorithm. These interval samples are used to weakly supervise the training of a neural net which approximates the true cost-to-go. Online, we use that learned cost-to-go as the terminal cost of a one-step model-predictive controller, which we solve via a small mixed-integer optimization. We demonstrate the LVIS approach on a cart-pole system with walls and a planar humanoid robot model and show that it can be applied to a fundamentally hard problem in feedback control--control through contact.