1 research outputs found
Imitation and Mirror Systems in Robots through Deep Modality Blending Networks
Learning to interact with the environment not only empowers the agent with
manipulation capability but also generates information to facilitate building
of action understanding and imitation capabilities. This seems to be a strategy
adopted by biological systems, in particular primates, as evidenced by the
existence of mirror neurons that seem to be involved in multi-modal action
understanding. How to benefit from the interaction experience of the robots to
enable understanding actions and goals of other agents is still a challenging
question. In this study, we propose a novel method, deep modality blending
networks (DMBN), that creates a common latent space from multi-modal experience
of a robot by blending multi-modal signals with a stochastic weighting
mechanism. We show for the first time that deep learning, when combined with a
novel modality blending scheme, can facilitate action recognition and produce
structures to sustain anatomical and effect-based imitation capabilities. Our
proposed system, can be conditioned on any desired sensory/motor value at any
time-step, and can generate a complete multi-modal trajectory consistent with
the desired conditioning in parallel avoiding accumulation of prediction
errors. We further showed that given desired images from different
perspectives, i.e. images generated by the observation of other robots placed
on different sides of the table, our system could generate image and joint
angle sequences that correspond to either anatomical or effect based imitation
behavior. Overall, the proposed DMBN architecture not only serves as a
computational model for sustaining mirror neuron-like capabilities, but also
stands as a powerful machine learning architecture for high-dimensional
multi-modal temporal data with robust retrieval capabilities operating with
partial information in one or multiple modalities