How people and robots work together, and how much a person should rely on what a robot says. The diary has few papers here so far; most of what I have comes from projects, such as GoGet and the talking cane in Robotics Enterprise.
Concepts
- Human-robot trust: calibrating how much to rely on a robot team.
- Disagreement between models, filed under perception.
- Evaluating language models, filed under robot learning.
Open questions
- How can an interface show that one robot in twelve is probably lying, without too many false alarms?
- What should an explanation from a robot team look like?
Papers
| Paper | Year | Venue | Domain | Threads |
|---|---|---|---|---|
| Brain2Qwerty v2 | 2026 | arXiv 2026 (Meta AI) | general | robustness |
| Semantic DoS | 2026 | arXiv preprint 2026 | general | robustness |
| Whisper | 2023 | ICML 2023 (arXiv 2022) | general | perception |
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Other subfields: perception, mapping and localisation, semantic mapping, planning and navigation, control, robot learning, multi-robot systems, safety and verification, security, field robotics.