CASSL: Curriculum Accelerated Self-Supervised Learning

TL;DR

CASSL introduces a curriculum-based self-supervised learning method that improves high-dimensional control policies by focusing training on control parameters in a sequence guided by sensitivity analysis, demonstrated on robotic grasping.

Contribution

The paper proposes a novel curriculum learning approach for self-supervised training of high-dimensional control policies, guided by variance-based sensitivity analysis.

Findings

CASSL outperforms baseline methods with up to 14% improvement.

Significant generalization to novel objects.

Effective training on complex multi-fingered grasping tasks.

Abstract

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for high-dimensional control require either scaling up the data collection efforts or using a clever sampling strategy for training. We present a novel approach - Curriculum Accelerated Self-Supervised Learning (CASSL) - to train policies that map visual information to high-level, higher- dimensional action spaces. CASSL orders the sampling of training data based on control dimensions: the learning and sampling are focused on few control parameters before other parameters. The right curriculum for learning is suggested by variance-based global sensitivity analysis of the control space. We apply our CASSL framework to learning how to grasp using an adaptive, underactuated multi-fingered gripper, a challenging system to control. Our experimental results indicate that CASSL provides significant improvement and generalization compared to baseline methods such as staged curriculum learning (8% increase) and complete end-to-end learning with random exploration (14% improvement) tested on a set of novel objects.