Learning mimic impression for multi legged vehicle

It is well known that subsumption architecture (SSA) gives a great impact on the robot design methodology. However there are still some shortfalls on SSA due to the lack of learning ability. An SSA with learning ability is expected to produce a more powerful SSA, in terms of flexibility, robustness, etc. Here a new extension of SSA is proposed, which is a kind of distributed SSA with behaviour networks as the learning function. This specialized distributed SSA behaves like multi-autonomous agents. The proposed method can be partitioned into two layers: an upper layer and a lower layer. The upper layer performs the learning of mimic impression and in turn commands the SSA agents in the lower layers. Through the real world experiment the effectiveness and adaptability of the proposed architecture are shown