TY - GEN
T1 - Stochastic Optimal Control for Modeling Reaching Movements in the Presence of Obstacles
T2 - 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BIOROB 2018
AU - Singh, Arun Kumar
AU - Berman, Sigal
AU - Nisky, Ilana
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/9
Y1 - 2018/10/9
N2 - In many human-in-the-Ioop robotic applications such as robot-assisted surgery and remote teleoperation, predicting the intended motion of the human operator may be useful for successful implementation of shared control, guidance virtual fixtures, and predictive control. Developing computational models of human movements is a critical foundation for such motion prediction frameworks. With this motivation, we present a computational framework for modeling reaching movements in the presence of obstacles. We propose a stochastic optimal control framework that consists of probabilistic collision avoidance constraints and a cost function that trades-off between effort and end-state variance in the presence of a signal-dependent noise. First, we present a series of reformulations to convert the original non-linear and non-convex optimal control into a parametric quadratic programming problem. We show that the parameters can be tuned to model various collision avoidance strategies, thereby capturing the quintessential variability associated with human motion. Then, we present a simulation study that demonstrates the complex interaction between avoidance strategies, control cost, and the probability of collision avoidance. For ease of exposition, our simulations are restricted to 2D trajectories. The proposed framework can benefit a variety of applications that require teleoperation in cluttered spaces, including robot-assisted surgery. In addition, it can also be viewed as a new optimizer which produces smooth and probabilistically-safe trajectories under signal dependent noise.
AB - In many human-in-the-Ioop robotic applications such as robot-assisted surgery and remote teleoperation, predicting the intended motion of the human operator may be useful for successful implementation of shared control, guidance virtual fixtures, and predictive control. Developing computational models of human movements is a critical foundation for such motion prediction frameworks. With this motivation, we present a computational framework for modeling reaching movements in the presence of obstacles. We propose a stochastic optimal control framework that consists of probabilistic collision avoidance constraints and a cost function that trades-off between effort and end-state variance in the presence of a signal-dependent noise. First, we present a series of reformulations to convert the original non-linear and non-convex optimal control into a parametric quadratic programming problem. We show that the parameters can be tuned to model various collision avoidance strategies, thereby capturing the quintessential variability associated with human motion. Then, we present a simulation study that demonstrates the complex interaction between avoidance strategies, control cost, and the probability of collision avoidance. For ease of exposition, our simulations are restricted to 2D trajectories. The proposed framework can benefit a variety of applications that require teleoperation in cluttered spaces, including robot-assisted surgery. In addition, it can also be viewed as a new optimizer which produces smooth and probabilistically-safe trajectories under signal dependent noise.
UR - http://www.scopus.com/inward/record.url?scp=85056598349&partnerID=8YFLogxK
U2 - 10.1109/BIOROB.2018.8487783
DO - 10.1109/BIOROB.2018.8487783
M3 - Conference contribution
AN - SCOPUS:85056598349
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
SP - 997
EP - 1004
BT - BIOROB 2018 - 7th IEEE International Conference on Biomedical Robotics and Biomechatronics
PB - Institute of Electrical and Electronics Engineers
Y2 - 26 August 2018 through 29 August 2018
ER -