288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

December 2014, Vol. 26, No. 12, Pages 2669-2691
(doi: 10.1162/NECO_a_00662)
© 2014 Massachusetts Institute of Technology
Risk-Aware Control
Article PDF (408.89 KB)

Human movement differs from robot control because of its flexibility in unknown environments, robustness to perturbation, and tolerance of unknown parameters and unpredictable variability. We propose a new theory, risk-aware control, in which movement is governed by estimates of risk based on uncertainty about the current state and knowledge of the cost of errors. We demonstrate the existence of a feedback control law that implements risk-aware control and show that this control law can be directly implemented by populations of spiking neurons. Simulated examples of risk-aware control for time-varying cost functions as well as learning of unknown dynamics in a stochastic risky environment are provided.