Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space
This work addresses sample efficiency in robotic grasping, an incremental advancement in combining model-based and model-free reinforcement learning for domain-specific control tasks.
The paper tackles the problem of sample-efficient robotic grasping by introducing a learning-adaptive imagination approach that uses an ensemble of latent dynamics models and intrinsic rewards based on learning progress, resulting in significant improvements in sample efficiency and near-optimal performance in sparse reward environments.
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.