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Ruzena Bajcsy - Personalized Modeling for HRI

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2015 NSF NRI PI Meeting

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Ruzena Bajcsy - Personalized Modeling for HRI

  1. 1. Personalized Modeling for Human-Robot Interaction Ruzena Bajcsy Electrical Engineering & Computer Sciences University of California, Berkeley October 23, 2015
  2. 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
  3. 3. Human Model Classes Musculoskeletal Kinematic/ Dynamic Kinematic Agent Interaction
  4. 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. 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. 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. 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. 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. 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. 10. Ultrasound for Muscle Observation • External motion capture for pose of ultrasound • Muscle and tendon outlines visible Muscle
  11. 11. Ultrasound for Muscle Observation • External motion capture for pose of ultrasound • Muscle and tendon outlines visible Muscle
  12. 12. Ultrasound for Muscle Observation • External motion capture for pose of ultrasound • Muscle and tendon outlines visible Tendon
  13. 13. Ultrasound for Muscle Observation • External motion capture for pose of ultrasound • Muscle and tendon outlines visible Tendon
  14. 14. Ultrasound for Muscle Observation
  15. 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

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