Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction

TL;DR

This paper introduces a meta-learning approach using Bayesian optimization with parameter bounds reduction to improve hyperparameter tuning efficiency in developmental robotics, demonstrated on industrial bin-picking tasks with small optimization budgets.

Contribution

It presents a novel framework combining long-term memory, visual similarity, and bounds reduction for meta-learning in robotic hyperparameter optimization, enhancing efficiency and success rates.

Findings

Meta-learning improves success rate from 78.9% to 84.3%.

Reduced parameter bounds lead to faster convergence.

System performs well with only 30 optimization iterations.

Abstract

In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects. We present a developmental framework based on long-term memory and reasoning modules (Bayesian Optimisation, visual similarity and parameters bounds reduction) allowing a robot to use meta-learning mechanism increasing the efficiency of such continuous and constrained parameters optimizations. The new optimization, viewed as a learning for the robot, can take advantage of past experiences (stored in the episodic and procedural memories) to shrink the search space by using reduced parameters bounds computed from the best optimizations realized by the robot with similar tasks of the new one (e.g. bin-picking from an homogenous heap of a similar object, based on visual similarity of objects stored in the semantic memory). As example, we have confronted the system to the constrained optimizations of 9 continuous hyperparameters for a professional software (Kamido) in industrial robotic arm bin-picking tasks, a step that is needed each time to handle correctly new object. We used a simulator to create bin-picking tasks for 8 different objects (7 in simulation and one with real setup, without and with meta-learning with experiences coming from other similar objects) achieving goods results despite a very small optimization budget, with a better performance reached when meta-learning is used (84.3% vs 78.9% of success overall, with a small budget of 30 iterations for each optimization) for every object tested (p-value=0.036).