{"id":"W4295292714","doi":"10.2514/1.g006656","title":"Laboratory Experimentation of Spacecraft Robotic Capture Using Deep-Reinforcement-Learning–Based Guidance","year":2022,"lang":"en","type":"article","venue":"Journal of Guidance Control and Dynamics","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spacecraft; Reinforcement learning; Computer science; Artificial intelligence; Aerospace engineering; Robotics; Simulation; Control engineering; Robot; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003946928,0.0004438617,0.0003219631,0.0002449322,0.000502201,0.0003303857,0.0008908301,0.0008182856,0.00278182],"category_scores_gemma":[0.00102514,0.0002211191,0.0002915762,0.0002033564,0.000590146,0.000515109,0.00070726,0.0006708133,0.0004068241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004458907,"about_ca_system_score_gemma":0.0006723679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004432856,"about_ca_topic_score_gemma":0.005592998,"domain_scores_codex":[0.9998098,0.00002933792,0.000007598331,0.00004375699,0.00006377908,0.00004569406],"domain_scores_gemma":[0.9994462,0.0002345823,0.00004962639,0.0001111953,0.00008211095,0.00007619528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001433042,0.002363617,0.006097674,0.0004824936,0.0001800926,0.0007221863,0.000563454,0.6772681,0.2392767,0.005426746,0.003919964,0.06226587],"study_design_scores_gemma":[0.00019386,0.002423899,0.005984494,0.00002961409,0.00003216256,0.0001764379,0.0001737931,0.8833467,0.1026985,0.001755912,0.00313535,0.00004927961],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9300466,0.0001125148,0.05994299,0.0002033501,0.00006635177,0.0002083043,0.000493869,0.0009376229,0.007988411],"genre_scores_gemma":[0.9841139,0.00004137041,0.01296215,0.00003883641,0.000003989349,0.00007601596,0.000347305,0.00003029144,0.002386061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004432856,"threshold_uncertainty_score":0.009306073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004766659491340846,"score_gpt":0.2113842403551108,"score_spread":0.20661758086377,"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."}}