Collaborative perception and learning between robots and the cloud
Abstract/Contents
- Abstract
- Augmenting robotic intelligence with cloud connectivity is considered one of the most promising solutions to cope with growing volumes of rich robotic sensory data and increasingly complex perception and decision-making tasks. While the benefits of cloud robotics have been envisioned long before, we have historically lacked flexible methods to trade-off the benefits of cloud computing with end-to-end systems costs of network delay, cloud storage, human annotation time, and cloud-computing time. To address this need, this thesis introduces decision-theoretic algorithms that allow robots to significantly transcend their on-board perception capabilities by using cloud computing, but in a low-cost, fault-tolerant manner. The utility of these algorithms is demonstrated on months of field data and experiments on state-of-the-art embedded deep learning hardware. Specifically, for compute-and-power-limited robots, this thesis presents a lightweight model selection algorithm that learns when a robot should exploit low-latency on-board computation, or, when highly uncertain, query a more accurate cloud model. Then, I present a collaborative learning algorithm that allows a diversity of robots to mine their real-time sensory streams for valuable training examples to send to the cloud for model improvement. This thesis concludes by surveying a number of future research directions on the systems and theoretical aspects of networked system control, some of which extend beyond cloud robotics
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Chinchali, Sandeep Prasad |
|---|---|
| Degree supervisor | Katti, Sachin |
| Thesis advisor | Katti, Sachin |
| Thesis advisor | Pavone, Marco, 1980- |
| Thesis advisor | Brunskill, Emma |
| Thesis advisor | Sadigh, Dorsa |
| Degree committee member | Pavone, Marco, 1980- |
| Degree committee member | Brunskill, Emma |
| Degree committee member | Sadigh, Dorsa |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Sandeep P. Chinchali |
|---|---|
| Note | Submitted to the Computer Science Department |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Sandeep Prasad Chinchali
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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