Does a predictive gap-seeking planner recover the framing a follow camera loses when it avoids pedestrians?

What it found

capture-follow found that a repulsion-field controller trades framing for distance: at 0.1 people/m² a drone that ignores pedestrians frames 99.8% of the time with 523 contacts (within 0.5 m) per hour, and 67.8% with 161 once it avoids them. Here a sampler that predicts pedestrians 3 s ahead and picks a velocity that goes round and rejoins is run on the same crowds (8 seeds). Framing is not recovered at equal settings: the planner at a 1.5 m radius frames the drone 58.0% of the time (repulsion 67.8%) but with 36 contacts an hour, not 161. What it changes is the trade-off: on the drone’s framing-against-contacts curve the planner sits to the left of repulsion (89% framed at 281 contacts against 79% at 322; 58% at 36 against 58.5% at 92), and on ground platforms at 0.1 people/m² it cuts contacts from 326 to 21 (wheeled, flat plaza) with the same 40% framing. At 0.3 people/m² both controllers lose the frame (3 to 39%). Pedestrians ignore the platform and their velocities are known exactly, so the planner is an upper bound on perception. Platform figures are capture-follow’s assumed round numbers, not datasheets.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.