信息驱动的可供性发现以实现高效机器人操作
Information-driven Affordance Discovery for Efficient Robotic Manipulation
AI总结：
本文提出信息驱动的可供性发现方法 IDA，通过基于信息的度量引导智能体与环境定向交互，在仿真和真实机器人上以少量交互高效学习抓取、堆叠、开抽屉等视觉可供性，显著提升数据效率。
摘要：
机器人可供性提供了在给定情境下可以采取哪些动作的信息，能够辅助机器人操作。然而，学习可供性需要昂贵的大规模交互或演示标注数据集。在这项工作中，我们认为与环境进行良好定向的交互可以缓解这一问题，并提出一种基于信息的度量来增强智能体的目标并加速可供性发现过程。我们为该方法提供了理论论证，并在仿真和真实世界任务中对该方法进行了实证验证。我们的方法被命名为 IDA，能够高效发现多种动作原语的视觉可供性，例如抓取、堆叠物体或打开抽屉，在仿真中显著提升了数据效率，并且使我们能够在少量交互中、在配备 UFACTORY XArm 6 机械臂的真实世界装置上学习抓取可供性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
