{"id":"W2168826583","doi":"10.1109/robot.1989.100224","title":"On-line robot trajectory planning for catching a moving object","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Trajectory; Object (grammar); Heuristic; Robot; Computer science; Acceleration; Matching (statistics); Artificial intelligence; Computer vision; Robot end effector; Path (computing); Line (geometry); Degree (music); Planar; Nonlinear system; Control theory (sociology); Mathematics; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001586114,0.0001380138,0.0001475394,0.00006504617,0.00007264424,0.00002845583,0.00006743508,0.00006266916,0.00005193566],"category_scores_gemma":[0.00006604267,0.0001280106,0.000062942,0.0000638342,0.000004218114,0.00005263238,0.00000505944,0.00011404,0.000009602854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004821028,"about_ca_system_score_gemma":0.00001567965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006375114,"about_ca_topic_score_gemma":0.000008174377,"domain_scores_codex":[0.9993405,0.000009147146,0.0001579436,0.0001444187,0.00008439928,0.0002635663],"domain_scores_gemma":[0.9996077,0.0001574547,0.00001442099,0.0001434852,0.00001225445,0.00006462997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003055416,0.000008517258,0.000008651189,0.00003044581,0.00001511798,0.00000283586,0.0001369224,0.9390152,0.001377482,0.05853095,0.0001656889,0.0007051668],"study_design_scores_gemma":[0.0003500243,0.00007550667,0.0000189311,0.00003905481,0.00001014611,0.000007281077,0.0001453926,0.9915661,0.001567941,0.005827246,0.0001773787,0.0002149768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01120214,0.00007673514,0.9714377,0.000008982885,0.0004596029,0.0001602173,0.000002251754,0.0003004572,0.01635193],"genre_scores_gemma":[0.6377591,0.000004188777,0.3612556,0.0001390316,0.00006301347,0.00002819698,0.00000626832,0.00005658006,0.0006880524],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.626557,"threshold_uncertainty_score":0.5220122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02457761100789443,"score_gpt":0.2503044752604497,"score_spread":0.2257268642525553,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}