Deep Visual Constraints: Neural Implicit Models for Manipulation Planning From Visual Input
| Autor: | Jung-Su Ha, Danny Driess, Marc Toussaint |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Human-Computer Interaction Computer Science - Robotics Control and Optimization Artificial Intelligence Control and Systems Engineering Mechanical Engineering Biomedical Engineering Computer Vision and Pattern Recognition Robotics (cs.RO) Computer Science Applications |
| Zdroj: | IEEE Robotics and Automation Letters. 7:10857-10864 |
| ISSN: | 2377-3774 |
| Popis: | Manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasping and placing an object, or more general tool-use. To achieve such interactions, traditional approaches require hand-engineering of object representations and interaction constraints, which easily becomes tedious when complex objects/interactions are considered. Inspired by recent advances in 3D modeling, e.g. NeRF, we propose a method to represent objects as continuous functions upon which constraint features are defined and jointly trained. In particular, the proposed pixel-aligned representation is directly inferred from images with known camera geometry and naturally acts as a perception component in the whole manipulation pipeline, thereby enabling long-horizon planning only from visual input. Project page: https://sites.google.com/view/deep-visual-constraints Comment: IEEE Robotics and Automation Letters (RA-L) 2022 |
| Databáze: | OpenAIRE |
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