In the face of current modular robots that rely on manual coding to generate deformation instructions and are limited by binary instructions, this research proposes a Cognitive Modular Control (CMC) architecture inspired by ACT-R theory. Build simulation tool ReoForm to conduct experiments through simulation-based experimental design. The data results show that each module of the architecture demonstrates feasibility and universality in terms of time-to-completion, energy consumption, manipulability index and robustness indicators.

To improve the cognitive autonomy of humanoid robots, this research proposes a multi-scenario reasoning architecture to solve the technical shortcomings of multi-modal understanding in this field. It draws on simulation based experimental design that adopts multi-modal synthesis (visual, auditory, tactile) and builds a simulator "Mahā" to perform the experiment. The findings demonstrate the feasibility of this architecture in multimodal data.