Information-driven Affordance Discovery for Efficient Robotic Manipulation
This addresses the data efficiency problem in robotic manipulation for researchers and practitioners, offering a novel approach but with incremental improvements over existing methods.
The paper tackles the problem of expensive data requirements for learning robotic affordances by proposing an information-based measure to direct interactions, enabling efficient discovery of visual affordances for actions like grasping and stacking, with strong data efficiency improvements in simulation and real-world learning in a small number of interactions.
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.