Complex AI on Small Embedded Systems: Humanoid Robotics using Mobile Phones
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
Until recent years, the development of real-world humanoid robotics applications has been hampered by a lack of available mobile computational power. Unlike wheeled platforms, which can reasonably easily be expected to carry a payload of computers and batteries, humanoid robots couple a need for complex control over many degrees of freedom with a form where any significant payload complicates the balancing and control problem itself. In the last few years, however, an significant number of options for embedded processing suitable for humanoid robots have appeared (e.g. miniaturized motherboards such as beagle boards), along with ever-smaller and more powerful battery technology. Part of the drive for these embedded hardware breakthroughs has been the increasing demand by consumers for more sophisticated mobile phone applications, and these modern devices now supply much in the way of sensor technology that is also potentially of use to roboticists (e.g. accelerometers, cameras, GPS). In this paper, we explore the use of modern mobile phones as a vehicle for the sophisticated AI necessary for autonomous humanoid robots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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