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.

翻译：机器人可供性提供了在特定情境下可执行动作的信息，能够辅助机器人操作。然而，学习可供性需要昂贵的大型交互或演示标注数据集。本研究提出，通过与环境的定向交互可以缓解该问题，并基于信息论提出一种度量方法，以增强智能体的目标并加速可供性发现过程。我们提供了该方法的理论证明，并通过仿真与真实任务进行了实证验证。我们提出的方法（称为IDA）能够高效发现多种动作基元（如抓取、堆叠物体或打开抽屉）的视觉可供性，在仿真中显著提升了数据效率，并使我们能够在真实场景中通过少量交互学习抓取可供性——实验使用UFACTORY XArm 6机械臂平台完成。