35 research outputs found
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
A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control
This paper introduces a family of iterative algorithms for unconstrained
nonlinear optimal control. We generalize the well-known iLQR algorithm to
different multiple-shooting variants, combining advantages like
straight-forward initialization and a closed-loop forward integration. All
algorithms have similar computational complexity, i.e. linear complexity in the
time horizon, and can be derived in the same computational framework. We
compare the full-step variants of our algorithms and present several simulation
examples, including a high-dimensional underactuated robot subject to contact
switches. Simulation results show that our multiple-shooting algorithms can
achieve faster convergence, better local contraction rates and much shorter
runtimes than classical iLQR, which makes them a superior choice for nonlinear
model predictive control applications.Comment: 8 page
Learning a Structured Neural Network Policy for a Hopping Task
In this work we present a method for learning a reactive policy for a simple
dynamic locomotion task involving hard impact and switching contacts where we
assume the contact location and contact timing to be unknown. To learn such a
policy, we use optimal control to optimize a local controller for a fixed
environment and contacts. We learn the contact-rich dynamics for our
underactuated systems along these trajectories in a sample efficient manner. We
use the optimized policies to learn the reactive policy in form of a neural
network. Using a new neural network architecture, we are able to preserve more
information from the local policy and make its output interpretable in the
sense that its output in terms of desired trajectories, feedforward commands
and gains can be interpreted. Extensive simulations demonstrate the robustness
of the approach to changing environments, outperforming a model-free gradient
policy based methods on the same tasks in simulation. Finally, we show that the
learned policy can be robustly transferred on a real robot.Comment: IEEE Robotics and Automation Letters 201
Legged Robots
International audienc
A Comparative Analysis of Contact Models in Trajectory Optimization for Manipulation
In this paper, we analyze the effects of contact models on contact-implicit
trajectory optimization for manipulation. We consider three different
approaches: (1) a contact model that is based on complementarity constraints,
(2) a smooth contact model, and our proposed method (3) a variable smooth
contact model. We compare these models in simulation in terms of physical
accuracy, quality of motions, and computation time. In each case, the
optimization process is initialized by setting all torque variables to zero,
namely, without a meaningful initial guess. For simulations, we consider a
pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our
results demonstrate that the optimization based on the proposed variable smooth
contact model provides a good trade-off between the physical fidelity and
quality of motions at the cost of increased computation time.Comment: 6 pages, 7 figures, 4 tables, IROS 2018 camera-ready versio