Jan2000report

Information about Jan2000report

Published on January 4, 2008

Author: Alien

Source: authorstream.com

Content

Natural Tasking of Robots Based on Human Interaction Cues :  Natural Tasking of Robots Based on Human Interaction Cues Rodney Brooks Cynthia Breazeal Brian Scassellati MIT Artificial Intelligence Lab Objective: Instructing Robots Through Imitation:  Objective: Instructing Robots Through Imitation A commander in the field will be able to task a robot just as he tasks a soldier Robots will be useable without special training or programming skills Robots will be taskable in unique and dynamic situations Research Issues:  Research Issues Knowing what to imitate What sensory signals are relevant to the current task? Mapping between bodies How to convert sensory view of one agent to self-mapping? Correcting failures and recognizing success How can the robot automatically develop success measures? Chaining actions together How can the learned pieces be chained together into complete task sequences? Generalizing to more complex tasks How can the robot use invariants and common-sense knowledge to adapt learned sequences to new situations? Making the interaction intuitive for the human How can natural social signals make it intuitive for humans and robots to communicate with each other? Approach:  Approach Four aspects of our research methodology address these six research issues: Capitalize on social cues from the commander Build adaptive systems with a developmental progression to limit complexity Exploit the advantages of the robot’s physical embodiment Integrate multiple sensory and motor systems to provide robust and stable behavioral constraints Humanoid Robot Platforms:  Humanoid Robot Platforms Upper-torso humanoid 21 DOF Visual, vestibular, auditory, tactile, and kinesthetic senses Active vision head with facial expressions 6 DOF head/neck, 15 DOF expressions Visual and auditory sensing New Heads for Cog and Kismet:  New Heads for Cog and Kismet Developed under other funding (but used in MARS) Modeled after human anatomy. 8 DOF neck and eyes Very close to human range of motion and speed Assembly and initial testing completed for three systems Mobile Platform #1 : M4:  Mobile Platform #1 : M4 Force-controlled Quadruped walker constructed under another DARPA contract 19 DOF with visual, vestibular, thermal, and kinesthetic sensing Currently completing design specs Mobile Platform #2 : Coco:  Mobile Platform #2 : Coco Knuckle-walking gorilla-like robot 15 DOF with visual, auditory, kinesthetic, and tactile sensing Currently in assembly Development of Imitation Skills:  Schema Creation Social Script Sequencing Instructional Sequencing Development of Imitation Skills Face Finding Eye Contact Gaze Direction Gaze Following Recognizing Pointing Speech Prosody Intentionality Detector Recognizing Instructor’s Knowledge States Recognizing Beliefs, Desires, and Intentions Arm and Face Gesture Recognition Facial Expression Recognition Familiar Face Recognition Directing Instructor’s Attention Vocal Cue Production Motion Detector Depth Perception Object Saliency Object Segmentation Object Permanence Body Part Segmentation Human Motion Models Long-Term Knowledge Consolidation Expectation-Based Representations Attention System Turn Taking Task-Based Guided Perception Action Sequencing VOR/ OKR Smooth Pursuit and Vergence Kinesthetic Body Representation Multi-Axis Orientation Mapping Robot Body to Human Body Self-Motion Models Reaching Around Obstacles Object Manipulation Active Object Exploration Tool Use Robot Teaching Line-of-Sight Reaching Simple Grasping Current Research Topics:  Current Research Topics Perceiving people Finding faces Finding eyes Adaptive motor control Simulated muscular motor control Oscillator-based locomotion Training from non-linguistic vocal cues Recognizing prosody (getting feedback) Expressive vocalization system (giving feedback) Integration Occulo-motor control systems Imitation as a mechanism for self-recognition Social dynamics Finding Faces: 2 Methods:  Finding Faces: 2 Methods Raw image Face Detected Eye Located Ratio Template 1) Ratio Template : looks for structure of grayscale gradients to detect frontal views… also extracts eye locations 2) Oval Models : looks for a partial ellipse based on edge gradients to detect a variety of head orientations Adaptive Eye Finding:  Adaptive Eye Finding Trained on hand-labeled images from real environments Currently using this and the ratio-template eye finder to begin to extract gaze direction, which leads to an understanding of joint reference Multi-Layer Perceptron Heuristic filter Raw image Skin-Color Filter Target Selector Current Research Topics:  Current Research Topics Perceiving people Finding faces Finding eyes Adaptive motor control Simulated muscular motor control Oscillator-based locomotion Training from non-linguistic vocal cues Recognizing prosody (getting feedback) Expressive vocalization system (giving feedback) Integration Occulo-motor control systems Imitation as a mechanism for self-recognition Social dynamics Simulated Muscular Motor Control:  Simulated Muscular Motor Control Dynamical properties similar to those of human musculature, as opposed to typical position/velocity based control Compliance/equilibrium-point control provides model of muscles Fatigue model: joint strength is modulated by activity Simulated multi-joint muscles Oscillatory circuits that simulate spinal mechanisms Implementation: roaming bands of modelers Build sensory-motor correlation to generate functional models of the causal relationships between different systems Over time, will build up a "body image" Oscillator-based Locomotion:  Oscillator-based Locomotion Previous work on using neural oscillators for arm control Application of oscillators to control locomotion Coupling between the joints determines a gait Robust to changes in environment and starting conditions Simplifies high-level control Natural distributed control No explicit model of the dynamics is used Connections used to achieve trotting gait Oscillator-based Locomotion:  Output of connected oscillators Output of unconnected oscillators Oscillator-based Locomotion Tested in simulation Gait and motion determined by oscillator coupling Force-control provides robustness Current Research Topics:  Current Research Topics Perceiving people Finding faces Finding eyes Adaptive motor control Simulated muscular motor control Oscillator-based locomotion Training from non-linguistic vocal cues Recognizing prosody (getting feedback) Expressive vocalization system (giving feedback) Integration Occulo-motor control systems Imitation as a mechanism for self-recognition Social dynamics Recognizing Prosody:  Signal Processing: pitch contour MFCC energy 32 features: pitch mean, variance, slope, DMFCC, energy variance, etc. Training & Classification of Individual Features: Gaussian Mixture Model .623 bootstrapping procedure (Efron, 1993) Individual Feature Performance Sequential Forward Feature Selection Recognizing Prosody Fernald’s (1989,1993) cross-linguistic analysis of infant-directed speech suggests four different pitch contours: approval, attention, prohibition, and comfort. Re-implemented a classifier built by Slaney and McRoberts (1998) to automatically distinguish between approval, attention, and prohibition. Initial Prosody Results:  Initial Prosody Results Best performance using sequential forward selection all 32 features: original system: 57.5 % replicated system: 60.8 % 8 global features: 71.5 % Future work incorporate prior knowledge in selecting new features, such as utterance duration speaker-dependent classification Expressive Vocalization System:  Expressive Vocalization System Expressive parameters: Pitch Timing Voice quality Articulation Articulatory parameters Choose from 6 basic emotions: anger, disgust, fear, happy, sad, surprise, and calm... Self-generated utterances: phoneme-based in-line stress markers Manually entered utterances: text based. Provide expressive vocal feedback to aid learning Convert expressive parameters of voice (such as pitch and timing) into articulatory parameters for a speech synthesizer. Current Research Topics:  Current Research Topics Perceiving people Finding faces Finding eyes Adaptive motor control Simulated muscular motor control Oscillator-based locomotion Training from non-linguistic vocal cues Recognizing prosody (getting feedback) Expressive vocalization system (giving feedback) Integration Occulo-motor control systems Imitation as a mechanism for self-recognition Social dynamics Occulo-motor Control:  Occulo-motor Control Integrating target fixation with attention Skin Detector Color Detector Motion Detector Face Detector t Habituation Wide Frame Grabber Daemon Motion Control Daemon Right Frame Grabber Daemon Left Frame Grabber Daemon Right Foveal Camera Wide Camera Left Foveal Camera Eye-Neck Motors W W W W Attention Wide Tracker Foveal Tracker Foveal Disparity Smooth Pursuit & Vergence w/ neck comp. VOR Saccade w/ neck comp. Fixed Action Pattern Affective Postural Shifts w/ gaze comp. Arbitor Eye-Head-Neck Control Target Selector Conjunctive movement Disjunctive movement Ballistic movement reset m t,m Saccade target Smooth pursuit target Vergence target winning behavior target target Behaviors & Motivations Imitation and Self-recognition:  Imitation and Self-recognition Use imitation as a way of recognizing Robot’s own body Other socially responsive agents Interaction quality when the robot follows Interaction quality when the robot leads Social Dynamics:  Social Dynamics Models of social dynamics must be integrated with Perception Motor control Emotion models and expressive systems Future work Turn taking Social gaze Natural Tasking of Robots:  Natural Tasking of Robots Robots can be naturally tasked using human social cues Robots need to understand intuitive human interactions Robots need to map their own capabilities to those of the human instructor A commander in the field will be able to task a robot just as he tasks a soldier Robots will be useable without special training or programming skills Robots will be taskable in unique and dynamic situations Perceiving People finding faces finding eyes Adaptive Motor Control simulated muscular motor control oscillator-based locomotion Using non-linguistic vocal cues recognizing prosody expressive vocalization system Integration Acknowledgements:  Acknowledgements Eduardo Torres-Jarra Naoki Sadakuni Juan Velasquez Charlie Kemp Lijin Aryananda Matthew Marjanovic Jessica Banks Artur Arsenio Paul Fitzpatrick Paulina Varchavskaia Chris Morse Chris Scarpino Bryan Adams

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