16,719 research outputs found
Benchmarking Cerebellar Control
Cerebellar models have long been advocated as viable models
for robot dynamics control. Building on an increasing insight
in and knowledge of the biological cerebellum, many models have been
greatly refined, of which some computational models have emerged
with useful properties with respect to robot dynamics control.
Looking at the application side, however, there is a totally different
picture. Not only is there not one robot on the market which uses
anything remotely connected with cerebellar control, but even in
research labs most testbeds for cerebellar models are restricted to
toy problems. Such applications hardly ever exceed the complexity of
a 2 DoF simulated robot arm; a task which is hardly representative for
the field of robotics, or relates to realistic applications.
In order to bring the amalgamation of the two fields forwards, we
advocate the use of a set of robotics benchmarks, on which existing
and new computational cerebellar models can be comparatively tested.
It is clear that the traditional approach to solve robotics dynamics
loses ground with the advancing complexity of robotic structures;
there is a desire for adaptive methods which can compete as traditional
control methods do for traditional robots.
In this paper we try to lay down the successes and problems in the
fields of cerebellar modelling as well as robot dynamics control.
By analyzing the common ground, a set of benchmarks is suggested
which may serve as typical robot applications for cerebellar models
Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation
Many algorithms for control, optimization and estimation in robotics depend
on derivatives of the underlying system dynamics, e.g. to compute
linearizations, sensitivities or gradient directions. However, we show that
when dealing with Rigid Body Dynamics, these derivatives are difficult to
derive analytically and to implement efficiently. To overcome this issue, we
extend the modelling tool `RobCoGen' to be compatible with Automatic
Differentiation. Additionally, we propose how to automatically obtain the
derivatives and generate highly efficient source code. We highlight the
flexibility and performance of the approach in two application examples. First,
we show a Trajectory Optimization example for the quadrupedal robot HyQ, which
employs auto-differentiation on the dynamics including a contact model. Second,
we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly
moving obstacle in a go-to task by fast, dynamic replanning
Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification
In this paper, we propose a contact-implicit trajectory optimization (CITO)
method based on a variable smooth contact model (VSCM) and successive
convexification (SCvx). The VSCM facilitates the convergence of gradient-based
optimization without compromising physical fidelity. On the other hand, the
proposed SCvx-based approach combines the advantages of direct and shooting
methods for CITO. For evaluations, we consider non-prehensile manipulation
tasks. The proposed method is compared to a version based on iterative linear
quadratic regulator (iLQR) on a planar example. The results demonstrate that
both methods can find physically-consistent motions that complete the tasks
without a meaningful initial guess owing to the VSCM. The proposed SCvx-based
method outperforms the iLQR-based method in terms of convergence, computation
time, and the quality of motions found. Finally, the proposed SCvx-based method
is tested on a standard robot platform and shown to perform efficiently for a
real-world application.Comment: Accepted for publication in ICRA 201
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