1 / 21

Curious Reconfigurable Robots

Curious Reconfigurable Robots. Kathryn Merrick ITEE Seminar – Friday 29 th August Collaborators: Dayne Schmidt, Elanor Huntington. Overview. Why build curious robots? Background Curious robots for creative play Strengths and limitations Current and future work. Why Build Curious Robots?.

donnel
Télécharger la présentation

Curious Reconfigurable Robots

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Curious Reconfigurable Robots Kathryn Merrick ITEE Seminar – Friday 29th August Collaborators: Dayne Schmidt, Elanor Huntington

  2. Overview • Why build curious robots? • Background • Curious robots for creative play • Strengths and limitations • Current and future work

  3. Why Build Curious Robots? • Explore the scalability of existing curiosity algorithms • Build adaptive robots: • Fault tolerant • Able to use tools • Reconfigurable • Creative

  4. Adaptable Robots Big Dog (Boston Dynamics, 2008) (Bongard et al, 2006)

  5. Self-Reconfigurable Robots • Research focuses on: • Hardware • Communication • Navigation (University of Pennsylvania, 2008)

  6. Curious, Developmental Robots • Research focus is on behaviour • Static robot configuration Curious Aibo (Sony CSL, 2003-2008)

  7. Curious, Reconfigurable Robots • Assume a changing robot configuration • Focus on behavioural algorithms

  8. Perceiving the World • World is represented as a variable length string from a grammar, rather than a fixed length vector • World comprises states and actions S <device_data> <device_data>  <Sidata><device_data> | ε <Sidata>  <s><Sidata> | ε <s>  (<label>:<value>) <label>  A unique identifier <value>  Real number A <actions> <actions>  <AjActions><actions> | ε <AjActions>  A unique identifier

  9. Modelling Curiosity • Curiosity is modelled as a function of novelty and interest

  10. Curious Reinforcement Learning • Agent learns a mapping of states to actions and utility values • Agent selects best action most of the time and a random action some of the time Reward = Curiosity value

  11. Architecture for a Curious, Reconfigurable Robot Curious Agent Memory Perception Curiosity Learning Activation Agent Layer Abstract Sensor Abstract Actuator Resource Manager Device Layer Device Manager

  12. Curious Robots for Creative Play Explores relationship between structure and behaviour Creative thinking spiral (Resnick, 2007) Imagination, creativity, play, sharing and reflection

  13. Strengths • Robot is reconfigurable – sensors and effectors can be added or removed • New behaviour emerges for new structure • Relationship between structure and behaviour revealed

  14. S2 A2 T2 A1 T1 S3 A3 T3 S1 (a) (b) (c) Limitations • Not exhibiting the same cyclic behaviour seen in simulated agents • Cyclic behaviour harder to measure • Learning takes a long time

  15. Curious Social Force Models for Reconfigurable Robots CDF Project: Dayne Schmidt

  16. Measuring the Performance of Curious Robots • Characterising attention focus using bifurcation diagrams • A(move fwd port 4) • A(move fwd port 6) • A(move bkd port 4) • A(move bkd port 6) • A(stop port 4) • A(stop port 6) With Elanor Huntington

  17. Measuring a Curious, Reconfigurable Robot • Focus of attention shifts as robot is reconfigured • A(move fwd port 4) • A(move bkd port 4) • A(stop port 4) • A(move fwd port 6) • A(move bkd port 6) • A(stop port 6)

  18. s1 w11 w21 s2 A1 w31 s3 A2 w41 s4 A3 w51 s5 Learning Approaches for Curious Robots • Representing the state-action table as a neural network reduces memory requirements • Attention focus limited to simple tasks

  19. Alternatives to Curiosity? • Modelling behaviour cycles as intrinsic reward for learning • In natural systems behaviour cycles occur at biological, cognitive and social levels • Sleep-wake cycle, seasonal, tidal or lunar cycles • Learning cycle, habituation and recovery • Fashion cycles, sociological cycles (Ahlgren and Halberg, 1990)

  20. Behaviour Cycles as Intrinsic Reward • Advantages in natural systems include: • Anticipation, efficiency, competition, navigation • Creativity through exploration • Social self-advancement • Also potential advantages for artificial systems

  21. Conclusions • We have created a curious, reconfigurable robot: • Sensors and effectors can be added or removed • New behaviour emerges for new structure • Ongoing work for: • Understanding curiosity in complex environments • Learning speed and representation • Measurement of emergent behaviour • Alternatives to curiosity as motivation for reconfigurable robots

More Related