{"id":"W3129503641","doi":"10.1109/iros45743.2020.9341134","title":"Catch the Ball: Accurate High-Speed Motions for Mobile Manipulators via Inverse Dynamics Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Vector Institute","funders":"","keywords":"Workspace; Computer science; Sequential quadratic programming; Inverse dynamics; Quadratic programming; Trajectory; Ball (mathematics); Mobile manipulator; Mobile robot; Motion control; Control theory (sociology); Artificial intelligence; Robot; Control engineering; Mathematical optimization; Engineering; Mathematics; Control (management); Kinematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004756112,0.00100434,0.0006262556,0.0002761634,0.000325804,0.0004604742,0.0009357968,0.000872656,0.001367106],"category_scores_gemma":[0.001127613,0.0003752804,0.0002820373,0.0002528298,0.0006800531,0.0007693901,0.001144089,0.001162467,0.0005748878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002820083,"about_ca_system_score_gemma":0.000581663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002761312,"about_ca_topic_score_gemma":0.002107105,"domain_scores_codex":[0.9997926,0.00003100371,0.000007663602,0.0000449524,0.0001006376,0.00002321369],"domain_scores_gemma":[0.9997097,0.0001001889,0.00006683123,0.00004820661,0.00005161752,0.00002348045],"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":[0.0001565125,0.00009822982,0.0007920364,0.0001479106,0.00003677795,0.0001747153,0.0001781369,0.8012761,0.03204295,0.004272936,0.002198209,0.1586255],"study_design_scores_gemma":[0.000009001538,0.00005890259,0.0001231497,0.000005246422,0.000002533336,0.00002545856,0.000006836773,0.9964505,0.001832408,0.0008693147,0.0006126048,0.000003904201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0104318,0.0001507242,0.9876802,0.00007973709,0.00001675782,0.0000332284,0.00001499802,0.0007173434,0.0008752826],"genre_scores_gemma":[0.7166258,0.0002187033,0.279724,0.00009118448,0.00004206931,0.0001733906,0.000110302,0.0001284036,0.002886215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002761312,"threshold_uncertainty_score":0.005490482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03304808840585274,"score_gpt":0.2591825047532625,"score_spread":0.2261344163474097,"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."}}