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… 1

Anthropomorphic Robotics Machine Learning for Adaptive Control Noise 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 Motor Command Biomechanical Plant Sensory Data Estimator Sensory Apparatus 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 Noise 2

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 3

Cosine shading Texture Mapped 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 4

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 6

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 7

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 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

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) 9