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Personalized Modeling for
Human-Robot Interaction
Ruzena Bajcsy
Electrical Engineering & Computer Sciences
University of California, Berkeley
October 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 Classes
Musculoskeletal
Kinematic/
Dynamic
Kinematic
Agent
Interaction
Human Model Classes: Kinematic
Musculoskeletal
Kinematic/
Dynamic
Kinematic
Agent
Interaction
Goal
Model 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/Dynamic
Musculoskeletal
Kinematic/
Dynamic
Kinematic
Agent
Interaction
Goal
Model 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
𝜏∗
= arg min
𝜏∈𝑈
𝑓 𝜃, 𝜏
𝑠. 𝑡. 𝑔 𝜃, 𝜏 ≤ 0,
ℎ 𝜃 = 0
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
Goal
Combine a kinematic/dynamic model with a
nonlinear model of muscle characteristics to
predict biomechanical properties throughout
the human’s workspace
Application
Medical 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
Conclusions
Musculoskeletal
Kinematic/
Dynamic
Kinematic
Agent
Interaction
• 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

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Ruzena Bajcsy - Personalized Modeling for HRI

  • 1. Personalized Modeling for Human-Robot Interaction Ruzena Bajcsy Electrical Engineering & Computer Sciences University of California, Berkeley October 23, 2015
  • 2. 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
  • 4. Human Model Classes: Kinematic Musculoskeletal Kinematic/ Dynamic Kinematic Agent Interaction Goal Model human motion using a rigid-body kinematic model Application Automated Coaching/ Quantitative Outcome Measures Contributors: Qifei Wang, Gregorij Kurillo, and Ferda Ofli
  • 5. Human Model Classes: Kinematic/Dynamic Musculoskeletal Kinematic/ Dynamic Kinematic Agent Interaction Goal Model human motion using a rigid-body kinematic/dynamic model Applications Human-Robot Collaborative Manipulation Human Dynamic Stability Analysis Contributors: Aaron Bestick and Victor Shia
  • 6. 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
  • 7. Application: Collaborative Manipulation Personalized Human Mechanical Models Coupled Human- Robot Dynamical Models Optimal Robot Control Human Constraints Robot Constraints Task Constraints Human Ergonomic Cost 𝜏∗ = arg min 𝜏∈𝑈 𝑓 𝜃, 𝜏 𝑠. 𝑡. 𝑔 𝜃, 𝜏 ≤ 0, ℎ 𝜃 = 0 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
  • 8. Human Model Classes: Musculoskeletal Musculoskeletal Kinematic/ Dynamic Kinematic Discrete States Goal Combine a kinematic/dynamic model with a nonlinear model of muscle characteristics to predict biomechanical properties throughout the human’s workspace Application Medical Diagnostics
  • 9. 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
  • 10. Ultrasound for Muscle Observation • External motion capture for pose of ultrasound • Muscle and tendon outlines visible Muscle
  • 11. Ultrasound for Muscle Observation • External motion capture for pose of ultrasound • Muscle and tendon outlines visible Muscle
  • 12. Ultrasound for Muscle Observation • External motion capture for pose of ultrasound • Muscle and tendon outlines visible Tendon
  • 13. Ultrasound for Muscle Observation • External motion capture for pose of ultrasound • Muscle and tendon outlines visible Tendon
  • 14. Ultrasound for Muscle Observation
  • 15. Conclusions Musculoskeletal Kinematic/ Dynamic Kinematic Agent Interaction • 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

Editor's Notes

  1. Pictures: - Musculoskeletal – muscle modeling for design of assistive robotic devices Kinematic/Dynamic – human-robot collaborative manipulation Kinematic – Quantitative measures of physical performance for muscular dystrophy Agent Interaction – Driver modeling and prediction for autonomous cars
  2. -Applications could include manufacturing and industry
  3. Pictures: - Musculoskeletal – muscle modeling for design of assistive robotic devices Kinematic/Dynamic – human-robot collaborative manipulation Kinematic – Quantitative measures of physical performance for muscular dystrophy Agent Interaction – Driver modeling and prediction for autonomous cars