{"id":"W4401689372","doi":"10.1016/j.actaastro.2024.08.015","title":"Path planning of 6-DOF free-floating space robotic manipulators using reinforcement learning","year":2024,"lang":"en","type":"article","venue":"Acta Astronautica","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Air Canada; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Reinforcement learning; Motion planning; Path (computing); Robot manipulator; Computer science; Space (punctuation); Reinforcement; Control engineering; Control theory (sociology); Aerospace engineering; Simulation; Robot; Engineering; Artificial intelligence; Structural engineering; Control (management)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001979934,0.0002149887,0.0002873597,0.0001395427,0.00006559361,0.00007126698,0.0001550815,0.00007820531,0.00006851827],"category_scores_gemma":[0.00003496402,0.000211929,0.0001005974,0.0002057915,0.00002417683,0.0001634614,0.0000635562,0.0003071576,0.00001433839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001202877,"about_ca_system_score_gemma":0.00003207517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006549883,"about_ca_topic_score_gemma":0.000001412391,"domain_scores_codex":[0.9987344,0.00002677615,0.0003654679,0.0002017052,0.0002641932,0.0004074391],"domain_scores_gemma":[0.9995005,0.00008005049,0.00005360596,0.0002637107,0.00002393654,0.00007821406],"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":[0.000005623674,0.000004706422,0.002156659,0.0004122787,0.0001557912,0.00002102774,0.0009551267,0.9436975,0.04892419,0.001528202,0.0001274549,0.002011408],"study_design_scores_gemma":[0.0002235599,0.00006443842,0.0008208882,0.000821626,0.00007320038,0.00001331036,0.0008568814,0.9894157,0.00217084,0.00003058241,0.005295652,0.0002133415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7816026,0.005176293,0.2025604,0.0001076346,0.001369284,0.000440333,0.000004461267,0.0008867548,0.007852271],"genre_scores_gemma":[0.9969631,0.000008526784,0.002572599,0.000005086384,0.0001686623,0.000004158412,0.000005486366,0.00006511062,0.0002072654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2153605,"threshold_uncertainty_score":0.8642213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347645313637547,"score_gpt":0.2295659284175646,"score_spread":0.2160894752811891,"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."}}