82 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
A Whole-Body Pose Taxonomy for Loco-Manipulation Tasks
Exploiting interaction with the environment is a promising and powerful way
to enhance stability of humanoid robots and robustness while executing
locomotion and manipulation tasks. Recently some works have started to show
advances in this direction considering humanoid locomotion with multi-contacts,
but to be able to fully develop such abilities in a more autonomous way, we
need to first understand and classify the variety of possible poses a humanoid
robot can achieve to balance. To this end, we propose the adaptation of a
successful idea widely used in the field of robot grasping to the field of
humanoid balance with multi-contacts: a whole-body pose taxonomy classifying
the set of whole-body robot configurations that use the environment to enhance
stability. We have revised criteria of classification used to develop grasping
taxonomies, focusing on structuring and simplifying the large number of
possible poses the human body can adopt. We propose a taxonomy with 46 poses,
containing three main categories, considering number and type of supports as
well as possible transitions between poses. The taxonomy induces a
classification of motion primitives based on the pose used for support, and a
set of rules to store and generate new motions. We present preliminary results
that apply known segmentation techniques to motion data from the KIT whole-body
motion database. Using motion capture data with multi-contacts, we can identify
support poses providing a segmentation that can distinguish between locomotion
and manipulation parts of an action.Comment: 8 pages, 7 figures, 1 table with full page figure that appears in
landscape page, 2015 IEEE/RSJ International Conference on Intelligent Robots
and System
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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