Reservoir controllers design though robot-reservoir timescale alignment.

Area of Science:

• Robotics
• Control Systems
• Computational Neuroscience

Background:

• Reservoir computers, leveraging nonlinear dynamical systems, offer sample-efficient model-free control for underactuated robots by approximating inverse dynamics.
• Current reservoir controller development is hindered by repetitive experiments and the need for expertise in both robot and reservoir design.

Purpose of the Study:

• To propose a reliable design method for reservoir controllers by synchronizing reservoir dynamics timescales with robot dynamics.
• To enhance the efficiency and effectiveness of reservoir-based robot control.

Main Methods:

• Developed a timescale alignment test to synchronize reservoir dynamics with robot dynamics.
• Filtered ineffective reservoirs using the proposed timescale alignment method.
• Evaluated selected reservoirs on computational tasks (memory, parity) and robot trajectory tracking control.

Main Results:

• The timescale alignment test successfully filtered out 99% of ineffective reservoirs.
• Higher computational capability in reservoirs reduced robot control failure rates.
• Increased computational capability correlated with higher trajectory-tracking errors.

Conclusions:

• Timescale synchronization is a crucial factor for reliable reservoir controller design.
• A trade-off exists between control failure rate reduction and trajectory-tracking accuracy based on reservoir computational capability.
• The proposed method streamlines reservoir controller development and improves system reliability.