机器人通过互动探索学习物体可操作性，提升抓取与推动等任务成功率。
Learning Affordances from Interactive Exploration using an Object-level Map
- 利用物体级地图实现多视角下物体识别与追踪，提升交互数据质量。
- 相比无地图方法，交互数据更密集准确，使可操作性预测模型性能提升。
- 适合需要物理交互的机器人场景，如家务协助或仓储搬运。
真实环境中许多机器人任务需与物体进行物理交互，如抓取或推动。为成功执行这些操作，机器人需了解物体的可操作性，即其能执行的动作。为学习特定于机器人的可操作性预测模型，我们提出一种交互式探索流程，使机器人在探索未知环境时收集交互经验。该流程整合了物体级地图，使机器人能够识别不同物体实例，并跨多种视角追踪物体。相比不使用地图的现有方法，本方法生成更密集、更准确的可操作性标注。实验表明，该探索方式提升了探索效率，所获得的可操作性预测模型精度优于基线方法。
原文摘要 · Abstract (English)
Many robotic tasks in real-world environments require physical interactions with an object such as pick up or push. For successful interactions, the robot needs to know the object's affordances, which are defined as the potential actions the robot can perform with the object. In order to learn a robot-specific affordance predictor, we propose an interactive exploration pipeline which allows the robot to collect interaction experiences while exploring an unknown environment. We integrate an object-level map in the exploration pipeline such that the robot can identify different object instances and track objects across diverse viewpoints. This results in denser and more accurate affordance annotations compared to state-of-the-art methods, which do not incorporate a map. We show that our affordance exploration approach makes exploration more efficient and results in more accurate affordance prediction models compared to baseline methods.
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