{"id":"W2133031383","doi":"10.1109/iros.2011.6094874","title":"A computational approach for push recovery in case of multiple noncoplanar contacts","year":2011,"lang":"en","type":"article","venue":"2011 IEEE/RSJ International Conference on Intelligent Robots and Systems","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Context (archaeology); Stability (learning theory); A priori and a posteriori; Computer science; Set (abstract data type); Humanoid robot; Control theory (sociology); State (computer science); Algorithm; Artificial intelligence; Robot","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":[0.0005645305,0.0007586331,0.001471222,0.0006979294,0.0012771,0.001387086,0.002563591,0.001866511,0.004590115],"category_scores_gemma":[0.002134863,0.0006211518,0.001213085,0.000392564,0.001860648,0.001773528,0.002927088,0.001613828,0.0005254982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006960433,"about_ca_system_score_gemma":0.00127353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004624913,"about_ca_topic_score_gemma":0.004059897,"domain_scores_codex":[0.9996498,0.00006825129,0.00001825154,0.00008030748,0.0001376036,0.00004584225],"domain_scores_gemma":[0.9990296,0.0005796849,0.00009824159,0.0001367479,0.0000888745,0.00006679399],"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":[0.00004741576,0.00003197596,0.0002479152,0.00006444148,0.00002110428,0.0001667904,0.00007620571,0.9572077,0.001564334,0.03160387,0.0003112671,0.008656998],"study_design_scores_gemma":[0.000003346758,0.000006477665,0.0000219728,0.000002413821,0.000002109152,0.0000118665,0.00001092309,0.994774,0.000120587,0.004801436,0.0002409446,0.00000391559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01486921,0.0000732067,0.9809126,0.0001974032,0.00003305243,0.00005392665,0.00004683473,0.0001356518,0.003678109],"genre_scores_gemma":[0.5554336,0.0002722486,0.4365248,0.0001757136,0.0001239996,0.000498911,0.0002050285,0.0002129628,0.006552641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004624913,"threshold_uncertainty_score":0.01535547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0851985772062371,"score_gpt":0.2646175931648163,"score_spread":0.1794190159585792,"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."}}