Fixed-Reference Pose Residuals for Measuring Cross-Dataset Cue Transfer in Human-Robot Interaction Anticipation
We study this question with a fixed-reference pose residual (FRPR) model: a geometry predictor built from the person's bounding box and mask is trained and frozen, and a temporal network then learns from body pose an additive correction to its logit, so that every prediction splits exactly into a geometry term and a pose term.
ProofPaper ↗
Key points
- Social and service robots in public spaces need to anticipate which nearby person is about to approach and touch them, so that a response can be prepared before contact.
- It is largely unknown which cues support this anticipation when a model trained with one robot is used on another robot at a different site.
- Between two public egocentric datasets recorded by different robots, HUI360 and SSUP-A, with every choice made on source data, the pose correction raised average precision (AP) from 0.277 to 0.321 from SSUP-A to HUI360 and gave no measurable gain in the opposite direction; the same asymmetry held over a stronger, source-selected geometry reference.
- Post hoc, whether a person faces the camera kept its discriminative direction across datasets, whereas head pitch reversed.
Sources (1)
- [1]Fixed-Reference Pose Residuals for Measuring Cross-Dataset Cue Transfer in Human-Robot Interaction AnticipationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 04:23 PM
We study this question with a fixed-reference pose residual (FRPR) model: a geometry predictor built from the person's bounding box and mask is trained and frozen, and a temporal network then learns from body pose an additive correction to its logit, so that every prediction splits exactly into a geometry term and a pose term.
Social and service robots in public spaces need to anticipate which nearby person is about to approach and touch them, so that a response can be prepared before contact.
Extractive summary: sentences quoted from the sources.