Charlie Rides the Elevator -- Integrating Vision, Navigation and Manipulation towards Multi-floor Robot Locomotion
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the design, implementation and experimental evaluation of a semi-humanoid robotic system for autonomous multi-floor navigation. This robot, a Personal Robot 2 named Charlie, is capable of operating an elevator to travel between rooms located on separate floors. Our goal is to create a robotic assistant capable of locating points of interest, manipulating objects, and navigating between rooms in a multi-storied environment equipped with an elevator. Taking the elevator requires the robot to (1) map and localize within its operating environment, (2) navigate to an elevator door, (3) press the up or down elevator call button, (4) enter the elevator, (5) press the control button associated with the target floor, and (6) exit the elevator at the correct floor. To that end, this work integrates the advanced sensorimotor capabilities of the robot - laser range finders, stereo and monocular vision systems, and robotic arms - into a complete, task-driven autonomous system. While the design and implementation of individual sensorimotor processing components is a challenge in and of itself, complete integration in intelligent systems design often presents an even greater challenge. This paper presents our approach towards designing the individual components, with focus on machine vision, manipulation, and systems integration. We present and discuss quantitative results of our live robotic system, discuss difficulties faced and expose potential pitfalls.
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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