AI Paper Atlas

Research map / Probabilistic and scientific modeling / Molecules and proteins

Learning to Predict Intent from Gaze During Robotic Hand-Eye Coordination

AAAI 2017 · Probabilistic and scientific modeling › Molecules and proteins · approach: Multimodal trajectory prediction · 20 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
add eye and gaze-type features to claw features (ablation)intent classifiers · claw-game intent predictionaccuracyNo clear effect
combine feature sets (e.g. claw + eye vs claw alone) (ablation)intent classifiers · claw-game intent predictionaccuracy: claw alone 89% accuracyHurt
use eye position features instead of claw features (ablation)intent classifiers · claw-game intent predictionaccuracy: 89% -> about 80%Hurt
use only gaze type features (ablation)intent classifiers · claw-game intent predictionaccuracy: 89% -> 69%Hurt
lengthen prediction horizon (time offset) (ablation)intent classifiers · claw-game intent predictionaccuracy: -0.62%/sHurt
use SVM or logistic regression instead of C4.5 (ablation)classifier choice · claw-game intent predictionAUC: 0.861 -> 0.590 (SVM), 0.576 (mlogReg)Hurt
use anticipatory (offset) gaze features (ablation)intent classifiers · claw-game intent predictionaccuracyNo clear effect

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy