Sample-Based Hybrid Mode Control: Asymptotically Optimal Switching of Algorithmic and Non-Differentiable Control Modes

TL;DR

This paper presents a sample-based hybrid mode control method that optimally switches between algorithmic and non-differentiable control modes, providing strong performance guarantees for robotics tasks.

Contribution

It introduces a novel sample-based optimization approach for hybrid control mode switching, capable of synthesizing complex behaviors with proven performance guarantees.

Findings

Effective in real-world robotic scenarios

Achieves asymptotic optimality in mode switching

Handles complex, non-differentiable control modes

Abstract

This paper investigates a sample-based solution to the hybrid mode control problem across non-differentiable and algorithmic hybrid modes. Our approach reasons about a set of hybrid control modes as an integer-based optimization problem where we select what mode to apply, when to switch to another mode, and the duration for which we are in a given control mode. A sample-based variation is derived to efficiently search the integer domain for optimal solutions. We find our formulation yields strong performance guarantees that can be applied to a number of robotics-related tasks. In addition, our approach is able to synthesize complex algorithms and policies to compound behaviors and achieve challenging tasks. Last, we demonstrate the effectiveness of our approach in real-world robotic examples that require reactive switching between long-term planning and high-frequency control.