Towards Humanoids Using Personal Transporters: Learning to Ride a Segway from Humans
Bibliographic record
Abstract
Human bipedal locomotion is efficient, robust and versatile, but typically reserved to reach targets in the close vicinity. As soon as larger distances have to be covered, humans tend to rely on wheeled modes of transport in the form of cars, bikes, scooters etc. Having the flexibility to choose a personal transporter (PT) such as a Segway when needed, is also an interesting option for humanoids operating in the real world, but it requires the ability to control a device that has its own complex dynamics. In this paper, we synthesize controllers for the the humanoid robot REEM-C to ride a Segway in simulation, motivated by human Segway riding. We perform motion capture experiments of a human riding a Segway and identify human whole-body behavior as well as the Segway's internal controllers. We then show that the REEM-C can successfully generate translational, rotational and mixed motions of the Segway in simulation. The Segway is controlled by targeted motions of the REEM-C using an inverted pendulum based LQR controller for pitch control and an admittance controller for the LeanSteer to command a yaw-rate. After these promising simulation results, the next step will be implementation on a real Segway and the REEM-C humanoid.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".