{"id":"W4388740180","doi":"10.1109/lra.2023.3333741","title":"Improved Generalization of Probabilistic Movement Primitives for Manipulation Trajectories","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Probabilistic logic; Artificial intelligence; Generalization; Task (project management); Movement (music); Machine learning; Trajectory; Object (grammar); Parameterized complexity; GRASP; Point (geometry); Algorithm; Mathematics; Engineering","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":[0.001889808,0.001156468,0.001182222,0.0009179614,0.0004597118,0.0006913603,0.002426845,0.001268837,0.001847508],"category_scores_gemma":[0.008090151,0.0008577572,0.001422488,0.0009752686,0.00122849,0.00273342,0.002202631,0.00260827,0.0006789691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009479572,"about_ca_system_score_gemma":0.001253365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006373428,"about_ca_topic_score_gemma":0.005741862,"domain_scores_codex":[0.9989786,0.0002484886,0.00006294514,0.0003409827,0.0002897471,0.00007922415],"domain_scores_gemma":[0.997665,0.001129927,0.0002868994,0.0005854426,0.0002411596,0.00009150601],"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":[0.0000922098,0.00006697359,0.001053069,0.00008455418,0.00005620889,0.00008023834,0.0001630499,0.8658758,0.00752029,0.02002495,0.001109042,0.1038735],"study_design_scores_gemma":[0.000005194565,0.00002531154,0.0002215281,0.000004240987,0.00000461462,0.00002190634,0.000004590846,0.992245,0.0006223345,0.006553086,0.0002852858,0.000006821358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01208598,0.0001201435,0.9859839,0.0001207158,0.00001540634,0.00003071822,0.00006950427,0.000916747,0.000656965],"genre_scores_gemma":[0.7405019,0.0004100518,0.2545591,0.0001773971,0.00005553438,0.0002636467,0.0006129905,0.0003858329,0.003033551],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006373428,"threshold_uncertainty_score":0.01267266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02234872284605136,"score_gpt":0.2379757460452677,"score_spread":0.2156270231992164,"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."}}