Towards Autonomous Programming of Micro-Assembly Robotics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the strive towards miniaturized systems and the growing field of optical technologies, micro-assembly is becoming increasingly important. Micro-assembly is characterized by challenging processes that require sub-micron level positioning accuracy regardless of modeling and calibration errors in the manipulator system. Automating these processes requires not only profound expertise about the process itself but also highly skilled personnel for programming the micro-assembly robot since current interfaces lack intuitive programming methods and simulation capabilities. In this paper, we outline a roadmap towards autonomous programming by combining intuitive programming approaches with intelligent and self-learning algorithms. Following this roadmap, the user will be supported progressively by autonomous and intelligent sub-processes until the machine can finally program itself autonomously. Based on a systematic review of the current state of automated micro-assembly and simulation frameworks, we show the capabilities of current approaches and identify key enablers for an autonomous assembly. From these enablers, we derive modules building our proposed framework. Central aspects are the development of a holistic simulation and a data management, which include not only the robot with its sensor systems but also assembly-components. These form the foundation for offline programming and the usage of machine learning algorithms. In order to facilitate future research, we propose the utilization of the Robot Operating System 2 framework (ROS2) as a basis for autonomous programming adhering the principles of open-source and enabling seamless integration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it