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Importance of Voluntary behavior, attention control To attain cognitive behavior

Workshop of DC-Tales (Santorini), June. 3. Importance of Voluntary behavior, attention control To attain cognitive behavior - Toward future cognitive humanoid robot. Takamasa Koshizen Honda Research Institute Japan Inc. Content. Part 1: Humanoid robot - Asimo

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Importance of Voluntary behavior, attention control To attain cognitive behavior

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  1. Workshop of DC-Tales (Santorini), June.3 Importance of Voluntary behavior,attention control To attain cognitive behavior - Toward future cognitive humanoid robot Takamasa Koshizen Honda Research Institute Japan Inc.

  2. Content Part 1: Humanoid robot - Asimo Part 2: Basic concept model Part 3: Application concept model Cf. We will have brief demo (video) of our robot

  3. Humanoid robot Brain-like technology represented as attention and cross-modal integration allows the humanoid robot to obtain social cognitive aspects of human-like behavior • Highly kinematic behavior • Multimodal communication • Social Cognitive feature

  4. PFC-ACC interaction playing a key role in cognitive control by monitoring for occurrence of response conflict. ACC response conflict situations across mutiple modalities. Voluntary Behavioral Selection Based on Reward (Shima et al, 2000) PFC-ACC Interaction

  5. - Prefrontal cortex (PFC), neocortex region - most elaborated in primates, animals for taking diverse and flexible - PFC is NOT critical for performing simple, automatic behavior to an unexpected sound or movement - By contrast, PFC is important when the ‘top-down’ processing is needed - when behavior must be induced by internal states or intentions. Preparation of Forcoming Actions! Pochon et al., 2001 Statistical Nature !! Rainer G. and Miller, E.K, 1999 Prefrontal cortex

  6. Basic model concept Hikosaka et al., 1998

  7. Learning scheme • Learning schemes to combine • two different criteria, dedicated • from subcortical and neocortical • region in the brain • Learning schemes are • Reinforcement learning • Supervised learning • Statistical learning • How it could be merged • the different criteria by • the learning scheme?

  8. Bayesian Update Top-down Bottom-up Targetdetection Koshizen et al., 2000

  9. Self-motion estimates Koshizen et al., 2001

  10. w 1 ε小 ε大 r - 1 Cross Supramodality Hardware design to rewiring mechanism for cross-modal integration Koshizen et al., 2002 =∫w・ρds

  11. Critical Period and Rewiring Aparticular class of inhibitory connection emerged within Neocortex during the crerical period, to bring visual sensitivity Hensch et al., 1998 Critical period for establishing the binocular system is not only the matter of binding visual form and motion to form representation of a visible object, but also the matter of semantic understanding of object. How brain distinguish between the resemble objects that end up classifiedinto otherwise different groups? Proposed cross-modal rewiring system provides the hypothetical computation that each heterosensory modality is intrinsically bound, and the rewiring (inhibitory) network yields thus the functional meaning of their sensory inputs by calculating. The two-typed supramodality such as motion and form.

  12. Basic emotion and expression Application model concept Visuoarditory interaction Basic Model Concept ComplexKinematic Capability Ecological behavior

  13. Biological Motion Dorsal 動態視覚 PPC MT V1 STS V2 V4 PIT Ventral ITC 形態視覚

  14. Component learning

  15. Growing component

  16. Hierarchical system Koshizen et al., 2003 Expectation: Few components in a face could endow to best represent hidden variables (indicator components), which are employed to specify and identify the person

  17. Complex kinematic feature

  18. Mechanical designing artificial skin is used as stabilizing pressure sensor This action is supported by a passive flexible spinal cord that is realized via a spring/motor combination connection the lower body to the upper body. This passive spring system provides additional forward thrust and support in fast forward motion. Similar to the bending spinal cord of a cheetah during fast running Picture of the raw material for artificial skin. The material is available in various thickness (object shown here is 3mm). This could be liable for a robot of approx. 5 to 10 kg weight.

  19. Conclusion • -A-priori knowledge or selection criteria – voluntary behavior • The approach of a purely AI system or a purely ANN system in practice leads • to non-satisfactory results. A hybrid construction of both criteria and flexibility • -Multimodal interaction makes easier to sophisticate the knowledge and • selection criteria Interaction/learning/evolvement of the system will lead to better • criteria settings. For the system as well as creation of new criteria which in turn • excelled the behavioral mechanism (In this sense, attention considers the • predominant role of attention as shaping behavior motor output). • Cognitive function may be attained by extremely complex kinematics capabilities • such as grasping for objects, bending down and up, sitting down on objects. Such • complex kinematic feature provides frequent interaction among polymodal stimuli. • Attention will need to be mediated for segregating the important/non-important • -Statistical Nature of prefrontal cortex could be mechanism for making the • selections, as suggested the paper by Miller et al. (1999) • -Expectation is one of candidates for the criteria of top-down attention control

  20. Future prospects Voluntary behavior and the extremely complex kinematics capabilities is the basis for the emergence of cognitive functionality Koshizen et al, 2002 Expectant function Motor Control Kirchner et al., 2002 How the criteria (ex. Expectation) can be converged the different local criterions between intrinsic bound (subcortical) and experience dependency (cortical), through attention mediated from prefrontral cortex.

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