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（robotic affordances）提供了在给定情境下可执行操作的信息，能够辅助机器人操作。然而，学习可供性需要昂贵的大规模标注交互或演示数据集。本文提出，与环境进行良好导向的交互可以缓解这一问题，并提出一种基于信息的度量方法，以增强智能体的目标函数并加速可供性发现过程。我们从理论上论证了该方法的合理性，并在仿真和真实世界任务中进行了实证验证。我们的方法（命名为IDA）能够高效发现多种基本动作（如抓取、堆叠物体或打开抽屉）的视觉可供性，在仿真中显著提高了数据效率。在真实场景中，我们使用UFACTORY XArm 6机器人臂，通过少量交互即可学习抓取可供性。