Goal-Image Conditioned Dynamic Cable Manipulation through Bayesian Inference and Multi-Objective Black-Box Optimization

TL;DR

This paper introduces a Bayesian optimization approach for dynamic cable manipulation guided by goal images, effectively handling uncertainty and avoiding local minima, leading to improved accuracy over traditional methods.

Contribution

It proposes a novel goal-image conditioned manipulation method using Bayesian inference and black-box optimization, addressing uncertainty and multiple feasible configurations.

Findings

Improved accuracy in achieving target cable configurations.

Effective handling of uncertainty in dynamic manipulation.

Better detection of multiple feasible solutions.

Abstract

To perform dynamic cable manipulation to realize the configuration specified by a target image, we formulate dynamic cable manipulation as a stochastic forward model. Then, we propose a method to handle uncertainty by maximizing the expectation, which also considers estimation errors of the trained model. To avoid issues like multiple local minima and requirement of differentiability by gradient-based methods, we propose using a black-box optimization (BBO) to optimize joint angles to realize a goal image. Among BBO, we use the Tree-structured Parzen Estimator (TPE), a type of Bayesian optimization. By incorporating constraints into the TPE, the optimized joint angles are constrained within the range of motion. Since TPE is population-based, it is better able to detect multiple feasible configurations using the estimated inverse model. We evaluated image similarity between the target and cable images captured by executing the robot using optimal transport distance. The results show that the proposed method improves accuracy compared to conventional gradient-based approaches and methods that use deterministic models that do not consider uncertainty.