{"id":"W3122520957","doi":"10.1109/iros51168.2021.9636080","title":"Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Reinforcement learning; Robot; Imitation; Task (project management); Salient; Representation (politics); Unsupervised learning; Deep learning; Machine learning; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002542401,0.0002372129,0.0003322299,0.0001290135,0.0001992342,0.0004141887,0.0005421926,0.00009883063,0.0001294483],"category_scores_gemma":[0.0001256412,0.0002311897,0.0001040725,0.0001868487,0.00005459672,0.0003032375,0.0001515423,0.0003804751,0.00008153488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009474781,"about_ca_system_score_gemma":0.00005956552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001398928,"about_ca_topic_score_gemma":0.00002632275,"domain_scores_codex":[0.9976203,0.0002538443,0.0005897119,0.0006745888,0.0006608868,0.0002006855],"domain_scores_gemma":[0.9982833,0.0002791201,0.0004235558,0.0003911934,0.0005087446,0.0001141097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001719878,0.0007299303,0.01208136,0.00005894814,0.0002280145,0.00001506649,0.005020058,0.07775708,0.2676001,0.6056808,0.0005454042,0.03026604],"study_design_scores_gemma":[0.0003521585,0.0001399056,0.009897914,0.0003211269,0.00002108778,0.000009535812,0.0005029227,0.9613643,0.02051265,0.005592012,0.000938575,0.0003477783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5555636,0.00007708317,0.4391621,0.001009607,0.0007418142,0.00024829,0.00003259104,0.00007196212,0.003092855],"genre_scores_gemma":[0.9961653,0.00006345639,0.001546434,0.00005541139,0.0002299788,0.00005409046,0.0003008066,0.00001811141,0.001566434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8836073,"threshold_uncertainty_score":0.9427641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0406571803570758,"score_gpt":0.3241998143792468,"score_spread":0.2835426340221709,"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."}}