Robotics-Based Reconstruction and Synthesis of Human Motion Emel Demircan

Adviser: Professor Oussama Khatib Artificial Intelligence Laboratory, Stanford University

E = cm2 (1)

E = F TΦ(q)F (2)

Φ(q) = J(LT (q)N2cL(q))

−1JT (3)

⇒ JJT (4)

Φ(q) = J(LT (q)N2cL(q))

−1JT (5)

⇒ (JJT )−1 (6)

⇒ Φ(q) = J(LT (q)N2cL(q))

−1JT (7)

1

Physio-mechanical Advantage

Muscle Force Transmission to Operational Space Accelerations

Effort Minimization in Dynamic Skills

Posture-based Effort Minimization

JtT(Aq + b+ g + Γext) ⇒ Λtxt + µt + pt +Rt = Ft (1)

x = J(q)A(q)−1(Γ− JTc1Fext1 − JT

c2Fext2) (2)

0 < Γ < LTmmax (3)

1

E = cm2 (1)

E = gTΦ(q)g (2)

Φ(q) = J(LT (q)N2cL(q))

−1JT (3)

E = cm2 (4)

E = F TΦ(q)F (5)

Φ(q) = J(LT (q)N2cL(q))

−1JT (6)

1

E = cm2 (1)

E = gTΦ(q)g (2)

Φ(q) = J(LT (q)N2cL(q))

−1JT (3)

E = cm2 (4)

E = F TΦ(q)F (5)

Φ(q) = J(LT (q)N2cL(q))

−1JT (6)

1

E = cm2 (1)

E = gTΦ(q)g (2)

Φ(q) = J(LT (q)N2cL(q))

−1JT (3)

E = cm2 (4)

E = F TΦ(q)F (5)

Φ(q) = J(LT (q)N2cL(q))

−1JT (6)

1

E = cm2 (1)

E = gTΦ(q)g (2)

Φ(q) = J(LT (q)N2cL(q))

−1JT (3)

E = cm2 (4)

E = F TΦ(q)F (5)

Φ(q) = J(LT (q)N2cL(q))

−1JT (6)

1

E = cm2 (1)

E = gTΦ(q)g (2)

Φ(q) = J(LT (q)N2cL(q))

−1JT (3)

E = cm2 (4)

E = F TΦ(q)F (5)

Φ(q) = J(LT (q)N2cL(q))

−1JT (6)

1

i. Optimal movement is affected by physio-mechanics; ii. Elite athletes perform skills optimally; iii. Study the criteria used to optimize task-driven motions; iii. Compare and coach for improvement.

Each subject’s physiology can be reflected to the task dynamics using the operational space acceleration characteristics.

Elite performers achieve the optimum transmission from their available muscle induced torque capacity to the desired task (i.e.. max golf club velocity).

Human Motion Characterization

Actuator torque Bounds on maximum limits accelerations

Robotics methods showed that the athlete was getting the most acceleration with the least physical effort.

The ability of humans to move and coordinate their limbs in common tasks is remarkable. Through learning, humans discover how to best position their body, benefiting from their physiology shaped by: 1- force generating capacities of the muscles; 2- joint mechanics; 3- limb mechanics.

Motivation and Challenges

i. Scaling detailed musculoskeletal models to the human subject; ii. Reconstruction and redundancy resolution of human motion in presence of constraints; iii. Characterization of human postural behaviors and dynamic skills.

Robotics methodologies leveraged to provide novel musculoskeletal modeling, analysis and control methods deal with these challenges.

In this work, 1- we develop human performance metrics; 2- we exploit the information given by musculo- skeletal models; 3- we reconstruct and analyze human motion in a task-oriented simulation and control framework.

References

Khatib O., Demircan E., et al. J. of Physiology, 2009. Khatib O. Int J. of Robotics and Automation, 1987. Khatib O. Int. J. of Humanoid Robotics, 2004. Demircan E, et al. Advances in Robot Kinematics, 2008. Demircan E, et al. Advances in Robot Kinematics, 2010. Stanford Robotics: http://cs.stanford.edu/groups/manips/