With this we could start from the incorrect muscle movements you currently have and gradually show them being corrected. Action observation at its' finest. But this will never occur. Video at link.
https://news.ubc.ca/2017/07/31/breakthrough-software-teaches-computer-characters-to-walk-run-even-play-soccer/
Computer characters and eventually robots could learn complex motor
skills like walking and running through trial and error, thanks to a
milestone algorithm developed by a University of British Columbia
researcher.
“We’re creating physically-simulated humans that learn to move with
skill and agility through their surroundings,” said Michiel van de
Panne, a UBC computer science professor who is presenting this research
today at SIGGRAPH 2017, the
world’s largest computer graphics and interactive techniques
conference. “We’re teaching computer characters to learn to respond to
their environment without having to hand-code the required strategies,
such as how to maintain balance or plan a path through moving obstacles.
Instead, these behaviors can be learned.”
The work, called DeepLoco,
offers an alternative way to animate human movement in games and film
instead of the current method which makes use of actors and motion
capture cameras or animators. DeepLoco allows characters to
automatically move in ways that are both realistic and attentive to
their surroundings and goals. In the future, two or four-legged robots
could learn to navigate through their environment without needing to
hand-code the appropriate rules.
Using his algorithm, simulated characters have learned to walk along a
narrow path without falling off, to avoid running into people or other
moving obstacles, and even to dribble a soccer ball towards a goal.
The method makes advanced use of deep reinforcement learning, a type
of machine learning algorithm in which experience is gained through
trial and error and is informed by rewards. Over time, the system
progressively identifies better actions to take in given situations.
“It’s like learning a new sport,” said van de Panne. “Until you try
it, you don’t know what you need to pay attention to. If you’re learning
to snowboard, you may not know that you need to distribute your weight
in a particular way between your toes and heels. These are strategies
that are best learned, as they are very difficult to code or design in
any other way.”
The motion of humans and animals is governed not just by physics but
also control. While humans learn motor control through trial and error,
van de Panne says it’s hard to tell how much the algorithm mimics the
human learning process. After all, the computer program still learns
much more slowly than a human. He began working on this type of motor
learning problem when he had children; they are now 17 and 20.
“I distinctly remember wondering who will learn agile walking and
running skills first: my son, daughter or the algorithm?” he said. “My
son and daughter beat me by a long shot.”
For more information on DeepLoco, click here.
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