libSmart: an Open-Source Tool for Simple Integration of Deep Learning into Intelligent Robotic Systems
Bibliographic record
Abstract
Intelligent robotic systems can be empowered by advanced deep learning techniques. Robotic operations such as object recognition are well investigated by researchers involved in machine learning. However, these solutions have often led to ad-hoc implementation in experimental settings. Less reported is systematic implementation of deep learning models in industrial robots. The lack of standard implementation platforms has impeded widespread use of deep learning modules in industrial robots. It is of great importance to have development platforms that can coordinate several deep learning modules of a complex system. In this paper, a scalable deep-learning friendly robot task organization system named libSmart is introduced. Similar to ROS, the architecture of the proposed system allows users to plug and play various devices but the proposed architecture is also highly compatible with deep learning modules. Specifically, the deployment of deep learning models is handled using a novel data graph method with distributed computing. In this way, the computationally expensive training and inferencing processes of deep learning models can be handled with isolated accelerating hardware to reduce the overall system latency. Successful implementation of simultaneous object recognition and pose estimation by an industrial robot has been presented as a case study. The proposed system is open source for all users to build their own intelligent systems with customized deep-learning models. (https://github.com/RustIron/libSmart.git).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".