Information-driven Affordance Discovery for Efficient Robotic Manipulation

TL;DR

This paper introduces IDA, an information-based method that accelerates robotic affordance discovery by guiding interactions, reducing data needs for tasks like grasping and opening drawers in simulation and real-world scenarios.

Contribution

Proposes an information-driven approach to improve efficiency of robotic affordance learning, validated through theoretical analysis and empirical experiments in simulation and real-world settings.

Findings

Significantly improves data efficiency in simulation tasks.

Enables learning grasping affordances with few real-world interactions.

Effectively discovers visual affordances for multiple action primitives.

Abstract

Robotic affordances, providing information about what actions can be taken in a given situation, can aid robotic manipulation. However, learning about affordances requires expensive large annotated datasets of interactions or demonstrations. In this work, we argue that well-directed interactions with the environment can mitigate this problem and propose an information-based measure to augment the agent's objective and accelerate the affordance discovery process. We provide a theoretical justification of our approach and we empirically validate the approach both in simulation and real-world tasks. Our method, which we dub IDA, enables the efficient discovery of visual affordances for several action primitives, such as grasping, stacking objects, or opening drawers, strongly improving data efficiency in simulation, and it allows us to learn grasping affordances in a small number of interactions, on a real-world setup with a UFACTORY XArm 6 robot arm.