Predictive display for mobile manipulators in unknown environments using online vision-based monocular modeling and localization
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
To tele-operate a robot, visual feedback is critical. However, communication channel latency can delay feedback to the point where the operator is impeded in performing his task. This work presents a vision-based “predictive display” system that compensates for visual delay. The approach is online and relatively uncalibrated, thus it has the advantage of being useful in unknown environments and many applications. From monocular eye-in-hand video, we incrementally compute a 3D graphics model of the robot site in real time using our new technique. The method exploits free-space/occlusion constraints on the scene to produce a physically consistent mesh. Novel vantage points are immediately rendered in response to the operator's control commands, without waiting for delayed video. We implement a full prototype tele-operation system where the operator controls, via a PHANTOM Omni device, a Barrett WAM robot mounted on a mobile Segway. Experiments with this setup validate the efficacy of the proposed approach. We demonstrate significant improvement in task completion time with predictive display on a real robot, while our previous related results were established only in simulation.
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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