Towards Cognitive Robotics Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Christian Goerick Honda Research Institute Europe GmbH Patrick Emaase

Contents 1Introduction 2Towards an Architecture 3Task and Body Oriented Motion Control 4Visually and Behaviorally Oriented Learning 5 ALIS – Autonomous Learning and Interactive 6Conclusion © 2015, SNU CSE Biointelligence Lab., 2

The Big Picture How to realize Cognitve Robot © 2015, SNU CSE Biointelligence Lab., Cognitive Robot Architecture Vision, Behavior Schematics / Repre ALIS

Introduction Long term goals Create humanoid robot equipped with mechanisms for learning and development – dynamically, robustly Understand and re-create how human brain works Research vehicle: Humanoid robot PISA – Practical Intelligent Systems Architecture; Architecture: Strategic Organization and incremental systems Major issue: Learning and adaptation – interaction with real world © 2015, SNU CSE Biointelligence Lab.,

Towards an Architecture: PISA © 2015, SNU CSE Biointelligence Lab., Cognitive Robot Intelligent Behavior Learn and reason Achieves complex goals Acts, perceives, plans, anticipates

Motion © 2015, SNU CSE Biointelligence Lab.,

Task & Body Oriented Motion Control Identify task accurately, move easily - complex Have level of intelligence as humans & animals control effectors for tasks easy Have Body image – helps acting in complex task Desirable cognitive architecture: able to cognitively control relevant task parameters, leave “tedious” details to underlying levels of control © 2015, SNU CSE Biointelligence Lab.,

ASIMO Robot: Kinematics © 2015, SNU CSE Biointelligence Lab., Stable layer for motion control with motion interface has been established – solve collision Robot controlled by task level description, the coupling is performed by whole body controller Implements redundant control scheme considers all DoF at once

Visually and Behaviorally Oriented Learning Goal: Provide Humanoid with Interactive behavior, vision, adaptability Autonomous development mechanisms Interactive Learning mechanisms Emphasis: Principled combination of both (A, IL) Biologically motivated Interactive vision System Adaptive basic behavior – can learn and recognize © 2015, SNU CSE Biointelligence Lab.,

Active Vision System © 2015, SNU CSE Biointelligence Lab., Active: Recognizes images, re-plans view points Determine new direction based on saliency + previous gaze direction

Concept of space Two types of space: Peripersonal space and Extrapersonal space Peripersonal space establishes “Sharing Attention” User show object, system focus on shown entity Addressed scientific concepts Online learning Internal homeostatic control system Combination of both © 2015, SNU CSE Biointelligence Lab.,

ALIS: Autonomous Learning and Interacting System Has incremental hierarchical system comprising sensing and control elements System interacts in real time with users Architecture: hierarchical mimicking biological brain © 2015, SNU CSE Biointelligence Lab.,

S YSTEMATICA Framework S YSTEMATICA – For describing incremental hierarchical control architecture © 2015, SNU CSE Biointelligence Lab., n is identifiable unit X – full input space D – dynamics R – representations B – behavior space T – top-down info P – priority S – sensory space M – motor commands

S YSTEMATICA Sensory S n (X) and Behavior space B n (X) split into location and features aspects. Framework characterizes architecture, decomposes units n consisting of S n (x), D n, B n, R n, M n, P n, T m,n to allow system: Incremental learning Always act Provide representations and decompositions Necessary conditions to achieve SYSTEMATICA is hierarchical arrangement of sensory and behavior © 2015, SNU CSE Biointelligence Lab.,

Biological Embedding of S YSTEMATICA To achieve brain-like intelligence Synergistic interplay of diff. level of hierarchy Dynamic architecture Brain modeled as inhibition of sensory signals and motor commands Deeper communication between units plausible and beneficial Efficient in (re)-using est. representations & processes © 2015, SNU CSE Biointelligence Lab.,

A LIS : Architecture and Elements © 2015, SNU CSE Biointelligence Lab., Schematics of A LIS formulated from S YSTEMATICA ALIS represents incrementally integrated system Elements are hierarchically arranged Produce observable behavior

So far…. Goal: Create Brain Like Intelligence Motivation: Human brain, Concepts: Active Learning, adaptability, Autonomy Architecture: PISA, Systematica Achievement: Advanced Step in Innovative Mobility (ASIMO) {Humanoid} Challenges: Stability, incremental knowledge © 2015, SNU CSE Biointelligence Lab.,

Conclusion ALIS System has independent units built Incremental hierarchy yields combined performance enabling Combines autonomy and ability to learn, develop Towards Cognitive Robotics Researching and creating in an incremental and holistic fashion leads to better understanding of natural and artificial brain-like systems © 2015, SNU CSE Biointelligence Lab.,

Review Question What do we gain by pursuing task description and whole body control in cognitive architecture? Description of tasks in natural way than in joint space High level process don’t care about details of motion Motion range is extended incrementally Understand DoF redundancy in movement & correspondence to hand actions & adaption to force Solve acceptance problems with robots Self collision avoidance on the level of motion control © 2015, SNU CSE Biointelligence Lab.,

Review Question What do we gain by pursuing such kind of task description and whole body control in cognitive architecture? Description of tasks in natural way than in joint space. High level process don’t care about details of motion Motion range is extended incrementally – appearance of robot motion is naturally relaxed Understand DoF redundancy in movement and correspondence to hand actions & adaption to force Solve acceptance problems with robots Self collision avoidance on the level of motion control © 2015, SNU CSE Biointelligence Lab.,

Thank you for listening 21