# Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning

## TL;DR

This paper introduces a sample-efficient Bayesian optimization approach to enhance the robustness of humanoid robot trajectories against uncertainties, achieving reliable motions with minimal simulation or experimental data.

## Contribution

It presents a novel method combining trajectory optimization with Bayesian optimization to efficiently find robust motion parameters for humanoid robots.

## Key findings

- Successfully generates robust motions under various disturbances.
- Achieves robustness with fewer simulations or experiments.
- Demonstrates effectiveness across different uncertainty scenarios.

## Abstract

Trajectory optimization (TO) is one of the most powerful tools for generating feasible motions for humanoid robots. However, including uncertainties and stochasticity in the TO problem to generate robust motions can easily lead to an interactable problem. Furthermore, since the models used in the TO have always some level of abstraction, it is hard to find a realistic set of uncertainty in the space of abstract model. In this paper we aim at leveraging a sample-efficient learning technique (Bayesian optimization) to robustify trajectory optimization for humanoid locomotion. The main idea is to use Bayesian optimization to find the optimal set of cost weights which compromises performance with respect to robustness with a few realistic simulation/experiment. The results show that the proposed approach is able to generate robust motions for different set of disturbances and uncertainties.