Robotic models of active perception

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Unstructured social environments, e.g. building sites, release an overwhelming amount of information, yet behaviourally relevant variables can be not directly accessible. A key solutions found by nature to cope with such problems is Active Perception (AP), as is shown by many examples, such as the foveal anatomy and control of the eye. Effectively designing a system that finds and selects relevant information and understanding its interdependencies on related functions, such as learning, will have an impact on both Robotics and Cognitive Neuroscience.

The main insights coming from the development of two different Active Vision (AV) robotic models will be presented:

1) an information theoretic AV model for dynamic environments where achieving an effective behaviour requires the prompt recognition of the hidden states (e.g. intentions) and the interactions (e.g. attraction), and spatial relationships between the elements in the environment. This general framework is described in the context of social interaction with AV systems which support the anticipation of other agents’ goals [Ognibene & Demiris 2013] and the recognition of complex activities [Lee et al submitted];

2) a neural model of the development of AV strategies in ecological tasks, such as exploring and reaching rewarding objects in a class of similar environments, the agent world. This model shows that an embodied agent can autonomously learn what are the behaviourally relevant contingencies in its world and how to use them to direct its perception [Ognibene & Baldassarre 2014].

This talk will finally touch on recent developments with AP regarding: extension of Active Inference Framework to AP [Friston et al]; the active allocation of resources for perception in industrial contexts [Darwin EU FP7 Project]; improving perception through the design of body parameters [Sokran, Howard & Nanayakkara 2014]; haptic exploration [Konstantinova et al 2014] and guidance [Ranasinghe et al 2013].

for complete presentation with videos check this link: https://www.dropbox.com/s/ff3tqky90u0iwye/OgnibeneIsacs2014toShare.ppsx?dl=0

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Robotic models of active perception

  1. 1. Robo$c Models of Ac$ve Percep$on Dimitri Ognibene, PhD Laboratory for Morphological Computa:on and Learning (www.thrish.org)
  2. 2. To subs:tute humans in dangerous jobs is one of the main goals of robo:cs The ac$ons in these pictures are already possible for robots of today. However…..
  3. 3. Perceiving in these environments is very complex: • Unstructured • Changing • Many different objects of different scales and shapes • Occlusions • Other agents to perceive and coordinate with Currently only humans are able to cope with such level of perceptual complexity… And humans perceive ac$vely…
  4. 4. Active Perception Ognibene & Demiris 2013 • Robo:cs • Neuroscience • Automa:c Diagnosis • Smart Devices & Environments • Data mining
  5. 5. Foveal Vision (What does it mean to perceive ac:vely?) 7
  6. 6. Foveal Vision (What does it mean to perceive ac:vely?) Try to grasp an apple with foveal vision.. Seeing becomes like sampling and remembering
  7. 7. Foveal Vision (What does it mean to perceive ac:vely?) Try to grasp an apple with foveal vision.. Seeing becomes like sampling and remembering
  8. 8. Foveal Vision (What does it mean to perceive ac:vely?) Try to grasp an apple with foveal vision.. Seeing becomes like sampling and remembering
  9. 9. Foveal Vision (What does it mean to perceive ac:vely?) Try to grasp an apple with foveal vision.. Seeing becomes like sampling and remembering
  10. 10. Foveal Vision (What does it mean to perceive ac:vely?) Try to grasp an apple with foveal vision.. Seeing becomes like sampling and remembering
  11. 11. Foveal Vision (What does it mean to perceive ac:vely?) Try to grasp an apple with foveal vision.. Seeing becomes like sampling and remembering
  12. 12. Ac:ve Percep:on (AP) Issues* • Where to look? • What to remember? • When to stop looking and start ac:ng? – Enough informa:on? – Enough :me? – Acquired informa:on s:ll valid? *See also The Frame Problem
  13. 13. Where to look? Use only image sta:s:cs? Main limits of base saliency models are: • No I] & Baldi 2010 task informa:on • Do not consider limited field of view
  14. 14. Where To look? Informa:on on Demand Yarbus 1967 16
  15. 15. Where to look? Context and task informa:on used to drive percep:on to the target Vogel & de Freitas 2008
  16. 16. Unknown Task or Goal • Task/Goal depending on other agents’ presence/ goals • Mul:ple affordances required for the task Ognibene & Demiris IJCAI 2013
  17. 17. Ac:ve Percep:on and Mirror Neurons Can Motor Control System predict others’ 19 ac:ons? • Encode ac:on goal • Abstracts trajectory • Needs percep:ons
  18. 18. Human Robot Interaction as a Distributed Dynamic Event Ognibene & Demiris 2013
  19. 19. Predic:ve Ac:on Recogni:on Field of view Effec:ve Percep:on-­‐Environment Coupling is necessary for :mely Recogni:on and Survival Ognibene & Demiris 2013
  20. 20. Predic:ve Ac:on Recogni:on Field of view Effec:ve Percep:on-­‐Environment Coupling is necessary for :mely Recogni:on and Survival Ognibene & Demiris IJCAI 2013
  21. 21. Different hypotheses of target posi:on Perceive to reduce Field of view Equally probable, not seen uncertainty See also “Percep:ons as hypotheses: saccades as experiments, Friston et al. 2012” Ognibene & Demiris IJCAI 2013
  22. 22. Perceive to reduce Field of view Hand movement changes distribu:on uncertainty Ognibene & Demiris IJCAI 2013
  23. 23. Field of view Perceive to reduce uncertainty Saccade to target hypothesis Ognibene & Demiris IJCAI 2013
  24. 24. Field of view Perceive to reduce uncertainty No target at posi:on observed Ognibene & Demiris IJCAI 2013
  25. 25. Field of view Perceive to reduce uncertainty Update Distribu:on Ognibene & Demiris 2013
  26. 26. for each element i an observation oti which depends on the configuration Info of Gain the sensors Percep✓t. :The on Control states and for observations Inten:on are continuous variables. An:cipa:on every time step the goal of the system is to select the configuration minimise Minimizing the event expected uncertainty uncertainty (condiover :onal V entropy (quantified H(v|..)) by entropy ˆ✓t = argmin p(ot|o0...t

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