Towards Humanoids Using Personal Transporters: Learning to Ride a Segway from Humans
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
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it