jan peters cggcjanpetersnet apologies multiple postings call posters nips 2007 workshop robotics challenges machine learning dates 78 december 2007 organizers jan peters max planck institute biological cybernetics usc marc toussaint technical university berlin www httpwwwrobotlearningde email opgq66robotlearningde abstract submission deadline october 21 2007 acceptance notification october 26 2007 call posters abstract creating autonomous robots assist humans situations daily life great challenge machine learning aim long standing vision robotics artificial intelligence cognitive sciences yet achieve first step creating robots accomplish multitude different tasks triggered environmental context higher level instruction despite wide range machine learning problems encountered robotics main bottleneck towards goal lack interaction core robotics machine learning communities date many roboticists still discard machine learning approaches generally inapplicable inferior classical handcrafted solutions similarly machine learning researchers yet acknowledge robotics play role machine learning instance physics mathematics major application well driving force new ideas algorithms approaches fundamental problems encounter robotics equally inspire current research directions machine learning learning handling models eg robots task environments learning deep hierarchies levels representations eg sensor motor representations task abstractions regression highdimensional spaces model policy learning finding lowdimensional embeddings movement implicit generative model methods probabilistic inference task parameters vision eg 3d geometry manipulated objects integration multimodal information eg proprioceptive tactile vision state estimation causal inference probabilistic inference nonlinear nongaussian stochastic systems eg planning well optimal adaptive control robotics challenges inspire motivate new machine learning research well interesting field application standard ml techniques inversely current rise real physical humanoid robots robotics research labs around globe need machine learning robotics grown significantly machine learning succeed making robots fully adaptive likely able take real robots research labs real human inhabited environments future robots need able make proper use perceptual stimuli vision proprioceptive tactile feedback translate motor commands close complex loop machine learning needed various stages ranging sensorybased action determination highlevel plan generation motor control torque level among important problems hidden steps problems understood robotics machine learning point view including perceptuoaction coupling imitation learning movement decomposition probabilistic planning problems motor primitive learning reinforcement learning model learning motor control format goal oneday workshop bring together people interested robotics source inspiration new machine learning challenges work machine learning methods new approach robotics challenges robotics context among questions intend tackle reinforcement learning imitation active learning methods reinforcement learning scale domain robotics improve policies acquired imitation trial error turn many simple learned demonstrations proper policies knowledge cost function teacher help student statistical methods help generating actions actively influencing perception eg used plan visuomotor sequences minimize uncertainty scene image understanding methods extended provide probabilistic scene descriptions suitable motor planning motor representations control decompose human demonstrations elemental movements eg motor primitives learn efficiently possible build libraries basic movements demonstration create higherlevel structured representations abstractions based elemental movements structured eg hierarchical temporal stochastic models used plan sequencing superposition movement primitives probabilistic inference road towards composing complex action sequences simple demonstrations superpositions motor primitives coupling timing learnable generate compliant controls executing complex movement plans include superposition hierarchies elemental movements find learned versions prioritized hierarchical control learn control taskspace redundant robots presence underactuation complex constraints learn force hybrid control taskspace realtime model learning way cope executing tasks robots unmodeled nonlinearities manipulating uncertain objects unpredictable environmental interactions new regression techniques help realtime model learning improve execution tasks robots unmodeled nonlinearities manipulating uncertain objects unpredictable environmental interactions learning structured models representations kind probabilistic models provide compact suitable description realworld environments composed manipulable objects abstractions compact representations learnt sensorimotor data extract features sensorimotor data relevant motor control decision making eg extract visual features objects directly related manipulability affordance posters open posters posing problems machine learning presenting machine learning algorithms applications robotics deadline abstract submissions october 21 2007 notification october 26 2007 abstract submission deadline october 21 2007 acceptance notification october 26 2007 fri 08 aug 2008 061856 0200 uai nips 2007 workshop robotics challenges machine learning