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.
Abstract (translated)
机器人能力（Robotic affordances）提供了一个特定环境中可以采取的动作信息，有助于机器人操作。然而，了解能力需要花费大量昂贵的大型交互或演示数据集。在这项工作中，我们认为与环境的有效交互可以缓解这个问题，并提出一个基于信息的指标来增强代理者的目标，加速发现能力过程。我们提供了我们方法的理论和实证验证，不仅在模拟中，而且在现实世界的任务中验证了我们的方法。我们的方法，我们称之为IDA，能够有效发现几个动作原语，如抓取、叠放物体或打开抽屉，极大地提高了仿真数据的有效性，并且允许我们在少数交互中学习抓取能力，在一个带有UFACTORY XArm 6机器人手臂的现实生活中设置的学习机器人。
URL
https://arxiv.org/abs/2405.03865
