13 research outputs found
On Time Optimization of Centroidal Momentum Dynamics
Recently, the centroidal momentum dynamics has received substantial attention
to plan dynamically consistent motions for robots with arms and legs in
multi-contact scenarios. However, it is also non convex which renders any
optimization approach difficult and timing is usually kept fixed in most
trajectory optimization techniques to not introduce additional non convexities
to the problem. But this can limit the versatility of the algorithms. In our
previous work, we proposed a convex relaxation of the problem that allowed to
efficiently compute momentum trajectories and contact forces. However, our
approach could not minimize a desired angular momentum objective which
seriously limited its applicability. Noticing that the non-convexity introduced
by the time variables is of similar nature as the centroidal dynamics one, we
propose two convex relaxations to the problem based on trust regions and soft
constraints. The resulting approaches can compute time-optimized dynamically
consistent trajectories sufficiently fast to make the approach realtime
capable. The performance of the algorithm is demonstrated in several
multi-contact scenarios for a humanoid robot. In particular, we show that the
proposed convex relaxation of the original problem finds solutions that are
consistent with the original non-convex problem and illustrate how timing
optimization allows to find motion plans that would be difficult to plan with
fixed timing.Comment: 7 pages, 4 figures, ICRA 201
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
Nonlinear Stochastic Trajectory Optimization for Centroidal Momentum Motion Generation of Legged Robots
Generation of robust trajectories for legged robots remains a challenging
task due to the underlying nonlinear, hybrid and intrinsically unstable
dynamics which needs to be stabilized through limited contact forces.
Furthermore, disturbances arising from unmodelled contact interactions with the
environment and model mismatches can hinder the quality of the planned
trajectories leading to unsafe motions. In this work, we propose to use
stochastic trajectory optimization for generating robust centroidal momentum
trajectories to account for additive uncertainties on the model dynamics and
parametric uncertainties on contact locations. Through an alternation between
the robust centroidal and whole-body trajectory optimizations, we generate
robust momentum trajectories while being consistent with the whole-body
dynamics. We perform an extensive set of simulations subject to different
uncertainties on a quadruped robot showing that our stochastic trajectory
optimization problem reduces the amount of foot slippage for different gaits
while achieving better performance over deterministic planning
On the Hardware Feasibility of Nonlinear Trajectory Optimization for Legged Locomotion based on a Simplified Dynamics
Simplified models are useful to increase the computational efficiency of a
motion planning algorithm, but their lack of accuracy have to be managed. We
propose two feasibility constraints to be included in a Single Rigid Body
Dynamicsbased trajectory optimizer in order to obtain robust motions in
challenging terrain. The first one finds an approximate relationship between
joint-torque limits and admissible contact forces, without requiring the joint
positions. The second one proposes a leg model to prevent leg collision with
the environment. Such constraints have been included in a simplified nonlinear
nonconvex trajectory optimization problem. We demonstrate the feasibility of
the resulting motion plans both in simulation and on the Hydraulically actuated
Quadruped (HyQ) robot, considering experiments on an irregular terrain
Recent Progress in Legged Robots Locomotion Control
International audiencePurpose of review. In recent years, legged robots locomotion has been transitioning from mostly flat ground in controlled settings to generic indoor and outdoor environments, approaching now real industrial scenarios. This paper aims at documenting some of the key progress made in legged locomotion control that enabled this transition. Recent findings. Legged locomotion control makes extensive use of numerical trajectory optimization and its online implementation, Model Predictive Control. A key progress has been how this optimization is handled, with refined models and refined numerical methods. This led the legged locomotion research community to heavily invest in and contribute to the development of new optimization methods and efficient numerical software