Representation Matters: Improving Perception and Exploration for Robotics

TL;DR

This paper systematically evaluates various learned and hand-engineered representations in robotics tasks, revealing how properties like dimensionality, observability, and disentanglement influence their effectiveness for perception and exploration.

Contribution

It provides a comprehensive analysis of representation properties in robotics, guiding the selection of effective representations for control and auxiliary tasks.

Findings

Some representations perform comparably to simulator states as inputs.

Dimensionality impacts task generation but not input effectiveness.

Observability mainly affects input representations.

Abstract

Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domains with limited data such as robotics. Can a single generally useful representation be found? In order to answer this question, it is important to understand how the representation will be used by the agent and what properties such a 'good' representation should have. In this paper we systematically evaluate a number of common learnt and hand-engineered representations in the context of three robotics tasks: lifting, stacking and pushing of 3D blocks. The representations are evaluated in two use-cases: as input to the agent, or as a source of auxiliary tasks. Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement. We can significantly improve performance in both use-cases and demonstrate that some representations can perform commensurate to simulator states as agent inputs. Finally, our results challenge common intuitions by demonstrating that: 1) dimensionality strongly matters for task generation, but is negligible for inputs, 2) observability of task-relevant aspects mostly affects the input representation use-case, and 3) disentanglement leads to better auxiliary tasks, but has only limited benefits for input representations. This work serves as a step towards a more systematic understanding of what makes a 'good' representation for control in robotics, enabling practitioners to make more informed choices for developing new learned or hand-engineered representations.