Robots are a major part of our
future, and researchers around the world have been working hard at enabling
smooth locomotion styles in humanoid and legged robots alike. Now a team of
researchers from the University of Edinburgh in Scotland has put together a
framework for training humanoid robots to walk just like us, humans, by using
human demonstrations. The team's framework works off of a unique reward design
that utilizes motion caption data of humans walking as part of the training
process. It then combines this with two specialized hierarchical neural
architectures: a phased-function neural network (PFNN) and a mode adaptive
neural network (MANN).
The wonderful news about the
team's framework was that it even enabled the humanoid robots to operate on
uneven ground or external pushes. The team's findings suggest that expert
demonstrations, such as humans walking, can majorly enhance deep reinforcement
learning techniques for training robots on a number of different locomotion
styles. Ultimately, these robots could move just as swiftly and easily as
humans, also while achieving more natural and human-like behaviors. At the
moment all the research has been carried out through a simulation, the next
steps involve trying the framework out in real life.
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