تقرير
Spatial Reasoning via Deep Vision Models for Robotic Sequential Manipulation
| العنوان: | Spatial Reasoning via Deep Vision Models for Robotic Sequential Manipulation |
|---|---|
| المؤلفون: | Zhou, Hongyou, Schubert, Ingmar, Toussaint, Marc, Oguz, Ozgur S. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics |
| الوصف: | In this paper, we propose using deep neural architectures (i.e., vision transformers and ResNet) as heuristics for sequential decision-making in robotic manipulation problems. This formulation enables predicting the subset of objects that are relevant for completing a task. Such problems are often addressed by task and motion planning (TAMP) formulations combining symbolic reasoning and continuous motion planning. In essence, the action-object relationships are resolved for discrete, symbolic decisions that are used to solve manipulation motions (e.g., via nonlinear trajectory optimization). However, solving long-horizon tasks requires consideration of all possible action-object combinations which limits the scalability of TAMP approaches. To overcome this combinatorial complexity, we introduce a visual perception module integrated with a TAMP-solver. Given a task and an initial image of the scene, the learned model outputs the relevancy of objects to accomplish the task. By incorporating the predictions of the model into a TAMP formulation as a heuristic, the size of the search space is significantly reduced. Results show that our framework finds feasible solutions more efficiently when compared to a state-of-the-art TAMP solver. Comment: 8 pages, 8 figures, IROS 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2306.17053 |
| رقم الأكسشن: | edsarx.2306.17053 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |