Overcoming occlusions in semi-autonomous telepresence systems
Bibliographic record
Abstract
In this paper we present an occlusion handling system, applied to articulated telepresence platforms, that simultaneously reduces the control burden on the operator and ensures the continuity of the visual interaction. Articulated telepresence systems improve the sense of presence of the operator by allowing the operator to maintain facial contact even during a walking conversation. However, the additional control of the articulated system, along with controlling navigation and carrying on a conversation can distract the operator and degrade the telepresence experience for both parties. Thus, we propose a semi-autonomous system that, along with visual tracking, can help relieve the burden of managing the articulated platform by clearing occlusions that might arise between the telepresense platform and the second party (the interlocuter). In particular, we demonstrate the application of a lost target recovery algorithm (LTRA) in a telepresence system. As the nature of a telepresence system requires fast recovery from occlusions through smooth motions, we improve the applied LTRA performance by adopting a numerical Inverse Kinematics solver and introducing a Cartesian space sampling method to generate optimal clearing motions. Finally, to validate different occlusion clearing methods, we implement them on an articulated telepresense platform. Experimental results for various scenarios support the feasibility of our approach to quickly recover visual contact in telepresence systems communications.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".