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Author
Date
2021Type
- Doctoral Thesis
ETH Bibliography
yes
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Abstract
The generation of agile and dynamic motions for legged robots has long
been of interest in the fields of computer graphics and robotics. However,
planning motions to control these underactuated, nonlinear dynamical
systems has proven to be a difficult problem. Building on recent advances
in differentiable physical models and trajectory optimization, this thesis
presents several approaches to synthesizing motion controls for robots with
legs and wheels, as well as compliant robots.
We begin by introducing a computational framework for motion gen-
eration of legged-wheeled robots. The user can easily design a robotic
creature with an arbitrary arrangement of legs, motor joints, and various
types of end effectors, such as point feet, actuated and unactuated wheels.
Once the robot’s morphology is determined, the user can create and edit
motion targets, such as way points, using an interactive tool, while our tra-
jectory optimization method generates physically valid motion trajectories.
Finally, we fabricate prototypes designed with our system and show that
the generated motions can be applied to the real world.
Next, we extend our system with a warm start technique that dramatically
improves the convergence rate of the trajectory optimization. Using our
computational framework, we design and build an agile robot with legs
and wheels, AgileBot, which can be equipped with actuated wheels, roller-
blades or ice-skates. Our trajectory optimization generates various agile
motions, such as roll-walking, swizzling or skating, which are executed
on the physical prototype. Finally, we use our system to generate several
dynamic motions as reference trajectories for feedback control of a legged-
wheeled robot.
Lastly, we introduce a differentiable physics engine capable of handling
frictional contact for rigid and deformable objects in a unified framework.
We combine a smoothed contact model with implicit time integration and
sensitivity analysis to analytically compute derivatives with respect to the
simulation parameters. We use our differentiable simulation to perform
trajectory optimization that accounts for the full dynamics of a legged robot
with compliant actuators and soft feet. We also demonstrate applications of
our differentiable simulator to parameter estimation for deformable objects,
motion planning for robot manipulations, and efficient self-supervised
learning of control policies. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000527172Publication status
publishedExternal links
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Contributors
Examiner: Coros, Stelian
Examiner: Kry, Paul
Examiner: Erleben, Kenny
Examiner: Ijspeert, Auke
Publisher
ETH ZurichOrganisational unit
09620 - Coros, Stelian / Coros, Stelian
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ETH Bibliography
yes
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