Institute of Perception, Action
and Behaviour (IPAB)
Director: Prof. Sethu Vijayakumar
www.inf.ed.ac.uk
IPAB Research Areas and Strengths
• Anthropomorphic Robotics (Sethu Vijayakumar and
Subramanian Ramamoorthy)
• Biomimetic Robotics (Barbara Webb)
• Graphics and Animation (Taku Komura)
• Multiagent Systems (Subramanian Ramamoorthy
and Michael Herrmann)
• Computer Vision (Bob Fisher)
• Computational Motor Control (Sethu Vijayakumar)
…and there are many cross connections between
these areas…
Anthropomorphic Robotics
Machine Learning for Adaptive Control
Noise
• Data-driven Machine
Learning methods in
Sensing, Planning and
Control of Robotic
Systems are key for:
Motor Command
Controller
Biomechanical Plant
– Scalability to large
degrees of freedom
Sensory
Data
– Enabling adaptation
Estimator
Sensory Apparatus
4) Feedback
3) Sensors
Noise
Sensing and Feedback:
2)
Robotics
1) EMG control
Novel ways of learning sensory-motor
associations and using this to provide effective
feedback for use in prosthetics
In collaboration with Touch Bionics
Anthropomorphic Robotics
Machine Learning for Adaptive Control
Planning for Scalability
New algorithms for planning under
redundancy and dealing with variable
stiffness and damping.
Novel ways of transferring behaviour across
heterogeneous plants
Dynamics Learning and Actuation
Development of novel actuators. Online learning of
dynamics and exploiting natural dynamics in
energetically explosive tasks.
In collaboration with DLR, Germany and HONDA
Computer Vision
Sensors and Algorithms
• Innovative 3D video sensor specifications and
applications
• 25 frames/second 3D + colour: 3D head modeling
• 8 Mpixel 3D + colour: skin cancer segmentation
and diagnosis
• 500 frames/sec 3D + infrared: bat acoustic
behaviour analysis
Cosine shading
Texture Mapped
Online Educational
resources:
• CVonline + HIPR
• 700K direct accesses
Biomimetic Robotics
Understanding sensorimotor control
• Replicating auditory, visual and tactile
sensing systems of insects
• Algorithmic and neural models of
multimodal processing in insect
brains, implemented on robots
• Novel and influential methodology
Recent focus on:
• Navigation capabilities
• Learning circuits
Graphics and Animation
Interactive Characters
Motion planning for multiple characters
• Need to avoid collisions / penetrations
• New representation of movements based on
spatial relationships
Simulating Interactions in cooperative / competitive
environments
• The characters need to learn how to collaborate or
compete with human player
• Game theory, reinforcement learning
Neurorobotics
Self organisation of
• Criticality in neural networks
• Behaviour in robots
• General principle for exploratory control for robots with various
bodies + guidance by external goals
• Applied to bootstrap control in transradial hand prostheses
Robust Autonomy and
Multiagent Systems
Autonomous decision making over
time needing interaction with
• complex dynamics
• richly structured spaces
Humanoid robotics, esp.
locomotion, manipulation
Reactive control with
layered models & multiple
representations
• continual & large changes
• other strategic agents and
adversaries
Complex electronic markets
Novel strategies and models for
dealing with regime switches and
extended uncertainty
Multi-robot systems,
e.g., RoboCup
Strategic interaction
with adversaries
despite imprecise
model knowledge
Collaborations and Outreach
Industries, Research Labs
• Microsoft, HONDA (Robot Learning)
• Autodesk, Namco Bandai, Blackrock Studio
(Animation and Computer Games)
• AIST Japan (Car design)
• RIKEN
• ATR (Computational Motor Contol)
• Touch Bionnics (Prosthetics)