  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.
