www.inf.ed.ac.uk

Institute of Perception, Action and Behaviour (IPAB)Director: Prof. Sethu Vijayakumar

• 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…

IPAB Research Areas and Strengths

Machine Learning for Adaptive Control• Data-driven Machine

Learning methods in Sensing, Planning and Control of Robotic Systems are key for:

– Scalability to large degrees of freedom

– Enabling adaptation

Controller Biomechanical Plant

Sensory ApparatusEstimator

Motor Command

Sensory Data

Noise

Noise

1) EMG control

2) Robotics

3) Sensors

4) Feedback

Sensing and Feedback: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

Planning for ScalabilityNew algorithms for planning under redundancy and dealing with variable stiffness and damping.Novel ways of transferring behaviour across heterogeneous plants

Dynamics Learning and ActuationDevelopment of novel actuators. Online learning of dynamics and exploiting natural dynamics in energetically explosive tasks.

In collaboration with DLR, Germany and HONDA

Machine Learning for Adaptive Control

Anthropomorphic Robotics

• 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

Sensors and Algorithms

Cosine shading Texture Mapped

Online Educational resources:

• CVonline + HIPR

• 700K direct accesses

Computer Vision

• 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

Biomimetic Robotics

Recent focus on:

• Navigation capabilities

• Learning circuits

Understanding sensorimotor control

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

Interactive CharactersGraphics and Animation

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

Humanoid robotics, esp. locomotion, manipulation

Reactive control with layered models & multiple representations

Multi-robot systems, e.g., RoboCup

Strategic interaction with adversaries despite imprecise model knowledge

Complex electronic markets

Novel strategies and models for dealing with regime switches and extended uncertainty

Robust Autonomy and Multiagent Systems

Autonomous decision making over time needing interaction with

• complex dynamics

• richly structured spaces

• continual & large changes

• other strategic agents and adversaries

• 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)

Collaborations and OutreachIndustries, Research Labs