{"id":"W3194593943","doi":"10.1109/ro-man50785.2021.9515460","title":"RRT-SMP: Socially-encoded Motion Primitives for Sampling-based Path Planning","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Motion planning; Motion (physics); Planner; Benchmark (surveying); Computer science; Path (computing); Social force model; Robot; Random tree; Artificial intelligence; Social planner; Social robot; Human–computer interaction; Mobile robot; Engineering; Robot control; Computer network; Pedestrian; Geography","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":[0.0004132832,0.000174627,0.0002261797,0.00007897024,0.0002785957,0.0002291416,0.000461643,0.0001054224,0.00001323068],"category_scores_gemma":[0.0003527841,0.0001740672,0.0001182537,0.0003067036,0.00003245753,0.0002953761,0.0001157236,0.0001361522,0.00001862867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007263553,"about_ca_system_score_gemma":0.0004908815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007313343,"about_ca_topic_score_gemma":9.5813e-7,"domain_scores_codex":[0.9983584,0.00008985019,0.0002688202,0.0005652509,0.0003034612,0.0004141817],"domain_scores_gemma":[0.9986302,0.0004911105,0.0001152086,0.0004053632,0.0002501198,0.0001079577],"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":[0.00008085002,0.001451392,0.01078572,0.0004951191,0.0003486892,0.0006358197,0.01358232,0.3907554,0.02386887,0.3074146,0.01336968,0.2372116],"study_design_scores_gemma":[0.0009357961,0.0001028413,0.006355482,0.0001098664,0.00001470211,0.00001818069,0.0001728771,0.9677202,0.0143737,0.009017756,0.0007962108,0.0003824355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002267368,0.00008859302,0.9934878,0.001323927,0.0005869326,0.0002090669,0.000009555519,0.000411212,0.001615498],"genre_scores_gemma":[0.06557591,0.00000111588,0.9328589,0.0008170361,0.0001612031,0.00004000918,0.0000469858,0.00001622737,0.0004825644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5769648,"threshold_uncertainty_score":0.7098254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07116702792391091,"score_gpt":0.3240758882188842,"score_spread":0.2529088602949733,"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."}}