{"id":"W2169452306","doi":"10.1109/tro.2008.915445","title":"Toward a Natural Language Interface for Transferring Grasping Skills to Robots","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Task (project management); Set (abstract data type); GRASP; Robot; Human–computer interaction; Natural language; Computer science; Process (computing); Natural (archaeology); Interface (matter); Human–robot interaction; Artificial intelligence; Scale (ratio); Engineering; Programming language","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":[0.002837189,0.0007449996,0.0003072013,0.0003884271,0.0003178428,0.001216045,0.001182401,0.0009853602,0.003554458],"category_scores_gemma":[0.008599402,0.0002451081,0.0003656308,0.0001542123,0.0008188097,0.002302347,0.001050103,0.000813667,0.00131673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002057407,"about_ca_system_score_gemma":0.0004882332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003594043,"about_ca_topic_score_gemma":0.000341637,"domain_scores_codex":[0.9985983,0.0007794357,0.0000907967,0.000213957,0.0002509786,0.00006650321],"domain_scores_gemma":[0.9950792,0.003615783,0.0002780208,0.000224418,0.00060635,0.0001961873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009945326,0.001720903,0.004836601,0.001508089,0.00006128116,0.002167443,0.02569723,0.00343274,0.6479481,0.007165144,0.003734712,0.3007333],"study_design_scores_gemma":[0.001024713,0.0106538,0.02469598,0.0005745611,0.0003656015,0.009745845,0.0165644,0.2078592,0.6023541,0.01601992,0.1095851,0.0005566984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.406992,0.0002363708,0.5797602,0.0006427407,0.00006986949,0.0009572225,0.0001289963,0.004403013,0.006809623],"genre_scores_gemma":[0.3876366,0.0001900976,0.6049682,0.0004087197,0.00003376399,0.0008491016,0.000328762,0.0002875265,0.005297222],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003554458,"threshold_uncertainty_score":0.01500469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02760504622624198,"score_gpt":0.2626809769842045,"score_spread":0.2350759307579625,"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."}}