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

Maxime Petit 1 Emmanuel Dellandrea 1 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
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 hyper-parameters 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).
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Contributor : Maxime Petit <>
Submitted on : Thursday, July 30, 2020 - 12:00:22 PM
Last modification on : Sunday, August 2, 2020 - 6:01:30 AM


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  • HAL Id : hal-02909335, version 1


Maxime Petit, Emmanuel Dellandrea, Liming Chen. Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction. IEEE International Conference on Developmental Robotics and Learning and Epigenetic Robotics 2020 (IEEE ICDL-Epirob 2020), Oct 2020, Valparaiso, Chile. ⟨hal-02909335⟩



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