Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space

TL;DR

This paper introduces a novel learning-adaptive imagination method in latent space that enhances sample efficiency and performance in vision-based robotic grasping by using an ensemble of local dynamics models and intrinsic motivation.

Contribution

It proposes a new approach that accounts for model reliability in imagination, combining model-based and model-free reinforcement learning for better robotic grasping.

Findings

Significantly improves sample efficiency in robotic grasping tasks.

Achieves near-optimal performance in sparse reward environments.

Uses ensemble of local models to generate reliable imagined experiences.

Abstract

Combining model-based and model-free deep reinforcement learning has shown great promise for improving sample efficiency on complex control tasks while still retaining high performance. Incorporating imagination is a recent effort in this direction inspired by human mental simulation of motor behavior. We propose a learning-adaptive imagination approach which, unlike previous approaches, takes into account the reliability of the learned dynamics model used for imagining the future. Our approach learns an ensemble of disjoint local dynamics models in latent space and derives an intrinsic reward based on learning progress, motivating the controller to take actions leading to data that improves the models. The learned models are used to generate imagined experiences, augmenting the training set of real experiences. We evaluate our approach on learning vision-based robotic grasping and show that it significantly improves sample efficiency and achieves near-optimal performance in a sparse reward environment.