1. 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

2. Anthropomorphic Robotics
Machine Learning for Adaptive Control
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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
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3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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