Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)(2022)

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In order to enable robust operation in unstructured environments, robots should be able to generalize ma-nipulation actions to novel object instances. For example, to pour and serve a drink, a robot should be able to recognize novel containers which afford the task. Most importantly, robots should be able to manipulate these novel containers to fulfill the task. To achieve this, we aim to provide robust and generalized perception of object affordances and their associated manipulation poses for reliable manipulation. In this work, we combine the notions of affordance and category-level pose, and introduce the Affordance Coordinate Frame (ACF). With ACF, we represent each object class in terms of individual affordance parts and the compatibility between them, where each part is associated with a part category-level pose for robot manipulation. In our experiments, we demonstrate that ACF outperforms state-of-the-art methods for object detection, as well as category-level pose estimation for object parts. We further demonstrate the applicability of ACF to robot manipulation tasks through experiments in both simulation and real world environment.
affordance coordinate frames,object perception,clutter,manipulation-oriented
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