用于高效机器人操作的信息驱动可供性发现
Information-driven Affordance Discovery for Efficient Robotic Manipulation
AI总结：
本文提出信息驱动方法 IDA，通过基于信息的交互目标增强智能体学习，以少量交互高效发现抓取、堆叠等视觉可供性，并在仿真与 UFACTORY XArm 6 真机实验中验证。
摘要：
机器人可供性提供了在给定情境下可以采取何种动作的信息，能够辅助机器人操作。然而，学习可供性需要昂贵的、带有大量标注的交互或示教数据集。在本研究中，我们认为与环境进行有针对性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，从而加速可供性发现过程。我们为该方法提供了理论依据，并在仿真和真实世界任务中对其进行了实证验证。我们的方法命名为 IDA，能够为抓取、堆叠物体或打开抽屉等多种动作原语高效发现视觉可供性，在仿真中显著提升数据效率；同时，在使用 UFACTORY XArm 6 机械臂的真实世界装置中，该方法使我们能够通过少量交互学习抓取可供性。
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。
