























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 "Maha" to perform the experiment. The findings demonstrate the feasibility of this architecture in multimodal data. It provides reference experience for the exploration of cross-modal interaction strategies for humanoid robots in dynamic environments. In addition, multi-scenario reasoning simulates the high-level reasoning mechanism of the human brain to humanoid robots at the cognitive level. This new concept promotes cross-scenario practical task transfer and semantic-driven action planning. It heralds the future development of self-learning and autonomous behavior of humanoid robots in changing scenarios.
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。