Summary of Text-driven Affordance Learning From Egocentric Vision, by Tomoya Yoshida et al.
Text-driven Affordance Learning from Egocentric Vision
by Tomoya Yoshida, Shuhei Kurita, Taichi Nishimura, Shinsuke Mori
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to visual affordance learning for robots, allowing them to understand how to interact with objects. The conventional methods rely on pre-defined objects and actions, which are insufficient for capturing diverse interactions in real-world scenarios. The proposed method employs textual instruction to target various affordances for a wide range of objects, covering both hand-object and tool-object interactions. This approach learns contact points and manipulation trajectories from an egocentric view following textual instruction. The paper also introduces TextAFF80K, a large pseudo-training dataset comprising over 80K instances of contact points, trajectories, images, and text tuples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Robots need to understand how to interact with objects in the real world, but current methods are limited. This new approach uses text instructions to help robots learn about different ways to interact with objects, like using their hands or tools. The goal is to make robots smarter and more able to handle everyday situations. |
