Summary of Model Adaptation For Time Constrained Embodied Control, by Jaehyun Song et al.
Model Adaptation for Time Constrained Embodied Control
by Jaehyun Song, Minjong Yoo, Honguk Woo
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 MoDeC, a time constraint-aware embodied control framework using modular model adaptation, optimizes deep learning models for specific tasks and operational conditions. The framework formulates model adaptation as dynamic routing on a modular network, incorporating time restrictions as part of multi-task objectives. MoDeC outperforms other model adaptation methods in both performance and adherence to time constraints in robotic manipulation and autonomous driving applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoDeC is a new way for robots and cars to learn and make decisions quickly. It helps models adapt to different situations and deadlines, which is important for tasks like picking up objects or avoiding obstacles. The approach uses a special kind of neural network that can change its behavior based on the task and available time. |
Keywords
* Artificial intelligence * Deep learning * Multi task * Neural network
