Robot hands one step closer to human thanks to WMG AI algorithms

The Shadow Robot Dexterous Hand is comparable to a human hand,
reproducing all of its degrees of freedom

· While dexterous manipulation of objects is a fundamental
everyday task for humans, it is still very challenging for
autonomous robotic hands to master

· Researchers at WMG, University of Warwick, have developed
novel artificial intelligence algorithms so the robot can learn how
to manipulate objects just like humans do

· In simulated environments, the robotic hands learn on their
own how to coordinate movements and execute tasks like throwing a
ball to each other and spinning a pen

The Shadow Robot Dexterous Hand is a robot hand, with
size, shape and movement capabilities similar to those of a human
hand. To give the robotic hand the ability to learn how to
manipulate objects researchers from WMG, University of Warwick,
have developed new AI algorithms.

Robot hands can be used in many applications, such as
manufacturing, surgery and dangerous activities like nuclear
decommissioning. For instance, robotic hands can be very useful in
computer assembly where assembling microchips requires a level of
precision that only human hands can currently achieve. Thanks to
the utilization of robot hands in assembly lines, higher
productivity may be achieved whilst securing reduced exposure from
work risk situations to human workers.

In the paper,‘Solving Challenging Dexterous
Manipulation Tasks With Trajectory Optimisation and Reinforcement
, researchers Professor Giovanni Montana and Dr
Henry Charlesworth from WMG, University of Warwick
have developed new AI algorithms – or the “brain” – required
to learn how to coordinate the fingers’ movements and enable

Using physically realistic simulations of Shadow’s robotic
hand, the researchers have been able to make two hands pass and
throw objects to each other, as well as spin a pen between its
fingers. The algorithms however are not limited to these tasks but
can learn any task as long as it can be simulated. The 3D
simulations were developed using MuJoCo (Multi-Joint Dynamics with
Contact), a physics engine from the University of Washington.

The researchers’ approach uses two algorithms. Initially, a
planning algorithm produces a few approximate examples 

of how the hand should be performing a particular task. These
examples are then used by a reinforcement learning algorithm that
masters the manipulation skills on its own. By taking this
approach, the researchers have been able to produce significantly
better performance compared to existing methodologies. The
simulation environments have been made publicly available for any
researcher to use.

Now that the algorithms have been successful in the simulations,
Professor Montana’s team will continue to work closely with
Shadow Robot and test the AI methodology on real robotic hardware,
which could see the hand advance one step closer to use in the real
day to day life.

In a second paper, ‘PlanGAN: Model-based Planning
With Sparse Rewards and Multiple Goals’
, to be presented at
the 2021 NeurIPS conference, the WMG researchers have also
developed a novel and general AI approach that enables robots to
learn tasks such as reaching and moving objects, which will further
improve hand manipulation applications.

Giovanni Montana
, from WMG, University of Warwick comments:
“The future of digitalisation relies on AI algorithms that can
learn autonomously, and    to be able to develop algorithms that
give Shadow Robot’s hand the ability to operate like a real one
is without any human input is an exciting step forward. These
autonomous hands could be used in the future to deliver robotic
surgeons, to increase the productivity of assembly lines and to
replace humans in dangerous jobs such as bomb disposal.”


“In future work we will let the robots perceive the
environment as accurately as humans do, not only through computer
vision algorithms that can see the world, but through sensors that
detect temperature, force and vibrations so the robot can learn
what to do when it feels those sensations.”

Rich Walker, Managing Director of the Shadow Robot Company, in London,
“ When we started building dexterous hands, it was because there
was no way to get hold of one without building it! 20 years later,
we are now seeing researchers like Giovanni deliver the promise of
the hardware by creating algorithms clever enough to control the
robot hand – soon perhaps we will see super-human

Originally published by
Univeristy of Warwick |
December 3, 2020

Link to papers:
Solving Challenging Dexterous Manipulation Tasks with Trajectory
Optimisation and Reinforcement

PlanGAN: Model-based Planning with Sparse Rewards and Multiple

Shadow Robot Company: /