Trajectory Optimization under Contact Timing Uncertainties

TL;DR

This paper introduces a novel deterministic optimal control approach to handle contact timing uncertainties in robotics, improving robustness in locomotion and manipulation tasks without complex combinatorial computations.

Contribution

It presents a new formulation that converts stochastic contact timing problems into deterministic optimization, avoiding combinatorial complexity and ensuring robustness.

Findings

Demonstrates improved robustness over nominal control in simulations.

Avoids complementarity constraints and combinatorial explosion.

Applicable to locomotion and manipulation tasks.

Abstract

Most interesting problems in robotics (e.g., locomotion and manipulation) are realized through intermittent contact with the environment. Due to the perception and modeling errors, assuming an exact time for establishing contact with the environment is unrealistic. On the other hand, handling uncertainties in contact timing is notoriously difficult as it gives rise to either handling uncertain complementarity systems or solving combinatorial optimization problems at run-time. This work presents a novel optimal control formulation to find robust control policies under contact timing uncertainties. Our main novelty lies in casting the stochastic problem to a deterministic optimization over the uncertainty set that ensures robustness criterion satisfaction of candidate pre-contact states and optimizes for contact-relevant objectives. This way, we only need to solve a manageable standard nonlinear programming problem without complementarity constraints or combinatorial explosion. Our simulation results on multiple simplified locomotion and manipulation tasks demonstrate the robustness of our uncertainty-aware formulation compared to the nominal optimal control formulation.