The Foreseeable Future: Self-Supervised Learning to Predict Dynamic Scenes for Indoor Navigation
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Bibliographic record
Abstract
We present a method for generating, predicting, and using spatiotemporal occupancy grid maps (SOGM), which embed future semantic information of real dynamic scenes. We present an autolabeling process that creates SOGMs from noisy real navigation data. We use a 3-D–2-D feedforward architecture, trained to predict the future time steps of SOGMs, given 3-D Lidar frames as input. Our pipeline is entirely self-supervised, thus enabling lifelong learning for real robots. The network is composed of a 3-D back-end that extracts rich features and enables the semantic segmentation of the lidar frames, and a 2-D front-end that predicts the future information embedded in the SOGM representation, potentially capturing the complexities and uncertainties of real-world multiagent interactions. We also design a navigation system that uses these predicted SOGMs within planning, after they have been transformed into spatiotemporal risk maps. We verify our navigation system's abilities in simulation, validate it on a real robot, study SOGM predictions on real data in various circumstances, and provide a novel indoor 3-D lidar dataset, collected during our experiments, which includes our automated annotations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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