Ruzena Bajcsy - Personalized Modeling for HRI
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Transcript of Ruzena Bajcsy - Personalized Modeling for HRI
Personalized Modeling for Human-Robot Interaction
Ruzena BajcsyElectrical Engineering & Computer Sciences
University of California, BerkeleyOctober 23, 2015
Our Lab’s Motivation
We wish to understand the mechanics of human-robot interactions and to design algorithms for control-sharing between humans and autonomous systems.
Why is this problem important?
– A person is a complex kinematic/dynamic system with many degrees of freedom and parameters which vary from person to person
– Not all degrees of freedom are used in all activities
Human Model ClassesMusculoskeletal
Kinematic/
Dynamic
Kinematic
Agent Interaction
Micro
Macro
Human Model Classes: KinematicMusculoskeletal
Kinematic/
Dynamic
Kinematic
Agent Interaction
GoalModel human motion using a rigid-body kinematic model
Application
Automated Coaching/Quantitative Outcome Measures
Contributors: Qifei Wang, Gregorij Kurillo, and Ferda Ofli
Human Model Classes: Kinematic/DynamicMusculoskeletal
Kinematic/
Dynamic
Kinematic
Agent Interaction
GoalModel human motion using a rigid-body kinematic/dynamic model
Applications
Human-Robot Collaborative Manipulation
Human Dynamic Stability Analysis
Contributors: Aaron Bestick and Victor Shia
Application: Collaborative Manipulation
Goal: Enable intelligent control of robots providing direct physical assistance to humans
• Create unified model of the human-robot coupled mechanical system
• Predict intent of human operator based on physical cues
Application: Collaborative Manipulation
Personalized Human Mechanical Models
Coupled Human-Robot Dynamical
Models
Optimal Robot Control
Human Constraints
Robot Constraints
Task Constraints
Human Ergonomic
Cost
Key Neglected Aspect: Differences in constraints and cost functions between individual humans (e.g. age, disabilities, natural variation)Implicit Assumption: Differences between humans not a significant contributor to task variability Need personalized models
Human Model Classes: Musculoskeletal
Musculoskeletal
Kinematic/
Dynamic
Kinematic
Discrete States
GoalCombine a kinematic/dynamic model with a nonlinear model of muscle characteristics to predict biomechanical properties throughout the human’s workspace
ApplicationMedical Diagnostics
Dynamic Human Musculoskeletal Modeling• Data Acquisition– MRI Scans– DICOM Images with
Segmentation– Motion Capture– EMG Data
• Modeling– 2D/3D Visualization– Interactive Cleaning– Static, Kinematic, Dynamic
Scenarios– Physics based Dynamic
Deformation
MRI Data segmentation
Ultrasound for Muscle Observation
• External motion capture for pose of ultrasound
• Muscle and tendon outlines visible
Muscle
Ultrasound for Muscle Observation
• External motion capture for pose of ultrasound
• Muscle and tendon outlines visible
Muscle
Ultrasound for Muscle Observation
• External motion capture for pose of ultrasound
• Muscle and tendon outlines visible
Tendon
Ultrasound for Muscle Observation
• External motion capture for pose of ultrasound
• Muscle and tendon outlines visible
Tendon
Ultrasound for Muscle Observation
ConclusionsMusculoskeletal
Kinematic/
Dynamic
Kinematic
Agent Interaction
Micro
Macro
• Robotic technology has great utility for modeling and predicting human physical capabilities and limitations
• By modeling the human, we can improve shared control schemes for human-robot interaction