Masa-aki Sato

TL;DR: While the estimation performance of existing methods depends on model parameters whose determination is difficult, the BPCA method is free from this difficulty, and provides accurate and convenient estimation for missing values.

TL;DR: Simulation results demonstrate that the proposed new hierarchical Bayesian method appropriately resolves the inverse problem even if fMRI data convey inaccurate information, while the Wiener filter method is seriously deteriorated by inaccurate fMRI information.

TL;DR: Computer simulations show that training of the CPG can be successfully performed by the proposed CPG-actor-critic method, thus allowing the biped robot to not only walk stably but also adapt to environmental changes.

TL;DR: It is demonstrated that appropriate sensory feedback in the CPG-based control architecture can be acquired using the proposed method within a thousand trials by numerical simulations, and the robustness of the acquired controllers against environmental changes and variations in the mass properties of the robot is suggested.

TL;DR: This study proposes a learning scheme for a CPG controller called a C PG-actor-critic model, whose learning algorithm is based on a policy gradient method, and applies this method to autonomous acquisition of biped locomotion by a biped robot simulator.