Model-free incremental learning of the semantics of manipulation actions
We addressed the problem of on-line learning of the semantics of manipulations. This is the first attempt to apply reasoning at the semantic level for learning. Our framework is fully grounded at the signal level. We introduced a new benchmark with 8 manipulations including in total 120 samples. We evaluated the learned semantic models with 20 long manipulation sequences.
incremental learning semantics manipulation action semantic event chains