Stochastic Resonance emergence from a Minimalistic Behavioral Rule

The behavioral rule we propose can be formalized in a straightforward manner in a completely general way as well. Denoting by uⁱ_t the i-th component of a motor command of an agent at time t, the same component at time t+1 is given by

where R is a random variable such as a white gaussian noise, ηⁱ means the intensity of the noise in the i-th component of the motor command, and ΔA_t expresses how much the agent improved its state during the t-th time step.

We have confirmed and theoretically proven that the agent can follow a gradient of an evaluation function despite that the rule is extremely simple thanks to stochastic resonance.

Reference

• Shuhei Ikemoto, Fabio DallaLibera, Koh Hosoda, and Hiroshi Ishiguro, "Minimalistic Behavioral Rule derived from Bacterial Chemotaxis in a Stochastic Resonance Setup", Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, Vol. 85, No. 2, pp. 021905, 2012.
• Shuhei Ikemoto, Fabio DallaLibera, and Hiroshi Ishiguro, "Stochastic Resonance Emergence from a Minimalistic Behavioral Rule", Journal of Theoretical Biology, Vol. 273, No. 1, pp. 179-187, 2011.

Minimalistic Behavioral Rule for Adaptive Robotic Behaviors

The minimalistic behavioral rule is extremely simple and does not require almost all information about robot's hardwares and environments. Therefore, its property will be maintained even if the environment and/or the robot's hardware was drastically changed. In this research, we gave several severe hardware accidents of a mobile robot (such as blowouts, bend of axle shafts, and loss of control signals) and confirmed that the property of the minimalistic behavioral rule could be maintained.

Reference

• Fabio DallaLibera, Shuhei Ikemoto, Hiroshi Ishiguro, and Koh Hosoda, "Control of real-world complex robots using a biologically inspired algorithm", Artificial Life and Robotics, Vol. 17, No. 1, pp. 42-46, 2012.
• Fabio DallaLibera, Shuhei Ikemoto, Takashi Minato, Hiroshi Ishiguro, Emanuele Menegatti and Enrico Pagello, "Biologically inspired mobile robot control robust to hardware failures and sensor noise", RoboCup Symposium 2010.
• Fabio DallaLibera, Shuhei Ikemoto, Takashi Minato, Hiroshi Ishiguro, Emanuele Menegatti and Enrico Pagello, "A parameterless biologically inspired control algorithm robust to nonlinearities, dead-times and low-pass filtering effects", 2nd International Conference on Simulation, Modeling and Programming for Autonomous Robots, 2010.

Redundant Sensor System for Stochastic Resonance Tuning without Input Signal Knowledge

To detect weak signals smaller than a sensor resolution has been often studied as engineering applications of stochastic resonance. To exploit stochastic resonace, the noise intensity should be adequate to the intensity of the weak signal, but the intensity of the signal is probably unknown in advance.

In this research, we propose the method to optimize the noise intensity by using spurious correlation among redundant sensors without knowing the input signal.

Reference

• Shuhei Ikemoto, Fabio DallaLibera, Koh Hosoda, and Hiroshi Ishiguro, "Spurious Correlation as an Approximation of the Mutual Information between Redundant Outputs and an Unknown Input", Communications in Nonlinear Science and Numerical Simulation, Vol. 19, No. 10, pp. 3611-3616, 2014.
• Nagisa Koyama, Shuhei Ikemoto, and Koh Hosoda, "Parameter Tuning in the Application of Stochastic Resonance to Redundant Sensor Systems", Journal of robotics and mechatoronics, Vol. 27, No. 3, pp. 251-258, 2015.

Minimalistic Decentralized Control using Stochastic Resonance inspired from a Skeletal Muscle

Stochastinc resonance can be very useful for control of distributed systems consisting of very simple elements. In this research, we focused on an actuator consisting of elements that can only fully contract and fully relax as the same as skeletal muscles in our body, and proposed a very simple system to continuously control the output force.

Reference

• Shuhei Ikemoto, Yosuke Inoue, Masahiro Shimizu and Koh Hosoda, "Minimalistic decentralized control using stochastic resonance inspired from a skeletal muscle", IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013.
• Shuhei Ikemoto, Yosuke Inoue, Masahiro Shimizu and Koh Hosoda, "Minimalistic decentralized modelling for a skeletal muscle based on stochastic resonance", 6th International Symposium on Adaptive Motion of Animals and Machines, 2013.