266 research outputs found
Multi-contact Walking Pattern Generation based on Model Preview Control of 3D COM Accelerations
We present a multi-contact walking pattern generator based on preview-control
of the 3D acceleration of the center of mass (COM). A key point in the design
of our algorithm is the calculation of contact-stability constraints. Thanks to
a mathematical observation on the algebraic nature of the frictional wrench
cone, we show that the 3D volume of feasible COM accelerations is a always a
downward-pointing cone. We reduce its computation to a convex hull of (dual) 2D
points, for which optimal O(n log n) algorithms are readily available. This
reformulation brings a significant speedup compared to previous methods, which
allows us to compute time-varying contact-stability criteria fast enough for
the control loop. Next, we propose a conservative trajectory-wide
contact-stability criterion, which can be derived from COM-acceleration volumes
at marginal cost and directly applied in a model-predictive controller. We
finally implement this pipeline and exemplify it with the HRP-4 humanoid model
in multi-contact dynamically walking scenarios
A Soft Robotic Cover with Dual Thermal Display and Sensing Capabilities
We propose a new robotic cover prototype that achieves thermal display while
also being soft. We focus on the thermal cue because previous human studies
have identified it as part of the touch pleasantness. The robotic cover surface
can be regulated to the desired temperature by circulating water through a
thermally conductive pipe embedded in the cover, of which temperature is
controlled. Besides, an observer for estimating heat from human contact is
implemented; it can detect human interaction while displaying the desired
temperature without temperature sensing on the surface directly. We assessed
the validity of the prototype in experiments of temperature control and contact
detection by human hand
Sequential Hierarchical Least-Squares Programming for Prioritized Non-Linear Optimal Control
We present a sequential hierarchical least-squares programming solver with
trust-region and hierarchical step-filter with application to prioritized
discrete non-linear optimal control. It is based on a hierarchical step-filter
which resolves each priority level of a non-linear hierarchical least-squares
programming via a globally convergent sequential quadratic programming
step-filter. Leveraging a condition on the trust-region or the filter
initialization, our hierarchical step-filter maintains this global convergence
property. The hierarchical least-squares programming sub-problems are solved
via a sparse reduced Hessian based interior point method. It leverages an
efficient implementation of the turnback algorithm for the computation of
nullspace bases for banded matrices. We propose a nullspace trust region
adaptation method embedded within the sub-problem solver towards a
comprehensive hierarchical step-filter. We demonstrate the computational
efficiency of the hierarchical solver on typical test functions like the
Rosenbrock and Himmelblau's functions, inverse kinematics problems and
prioritized discrete non-linear optimal control
Efficient Lexicographic Optimization for Prioritized Robot Control and Planning
In this work, we present several tools for efficient sequential hierarchical
least-squares programming (S-HLSP) for lexicographical optimization tailored to
robot control and planning. As its main step, S-HLSP relies on approximations
of the original non-linear hierarchical least-squares programming (NL-HLSP) to
a hierarchical least-squares programming (HLSP) by the hierarchical Newton's
method or the hierarchical Gauss-Newton algorithm. We present a threshold
adaptation strategy for appropriate switches between the two. This ensures
optimality of infeasible constraints, promotes numerical stability when solving
the HLSP's and enhances optimality of lower priority levels by avoiding
regularized local minima. We introduce the solver ADM, an
alternating direction method of multipliers for HLSP based on nullspace
projections of active constraints. The required basis of nullspace of the
active constraints is provided by a computationally efficient turnback
algorithm for system dynamics discretized by the Euler method. It is based on
an upper bound on the bandwidth of linearly independent column subsets within
the linearized constraint matrices. Importantly, an expensive initial
rank-revealing matrix factorization is unnecessary. We show how the high
sparsity of the basis in the fully-actuated case can be preserved in the
under-actuated case. ADM consistently shows faster
computations times than competing off-the-shelf solvers on NL-HLSP composed of
test-functions and whole-body trajectory optimization for fully-actuated and
under-actuated robotic systems. We demonstrate how the inherently lower
accuracy solutions of the alternating direction method of multipliers can be
used to warm-start the non-linear solver for efficient computation of high
accuracy solutions to non-linear hierarchical least-squares programs
Multi-Contact Postures Computation on Manifolds
International audienceWe propose a framework to generate static robot configurations satisfying a set of physical and geometrical constraints. This is done by formulating nonlinear constrained optimization problems over non-Euclidean manifolds and solving them. To do so, we present a new sequential quadratic programming (SQP) solver working natively on general manifolds, and propose an interface to easily formulate the problems, with the tedious and error-prone work automated for the user. We also introduce several new types of constraints for having more complex contacts or working on forces/torques. Our approach allows an elegant mathematical description of the constraints and we exemplify it through formulation and computation examples in complex scenarios with humanoid robots
Singularity resolution in equality and inequality constrained hierarchical task-space control by adaptive non-linear least-squares
International audienceWe propose a robust method to handle kinematic and algorithmic singularities of any kinematically redundant robot under task-space hierarchical control with ordered equalities and inequalities. Our main idea is to exploit a second order model of the non-linear kinematic function, in the sense of the Newton's method in optimization. The second order information is provided by a hierarchical BFGS algorithm omitting the heavy computation required for the true Hessian. In the absence of singularities, which is robustly detected, we use the Gauss-Newton algorithm that has quadratic convergence. In all cases we keep a least-squares formulation enabling good computation performances. Our approach is demonstrated in simulation with a simple robot and a humanoid robot, and compared to state-of-the-art algorithms
Multi-contact Planning on Humans for Physical Assistance by Humanoid
International audienceFor robots to interact with humans in close proximity safely and efficiently, a specialized method to compute whole-body robot posture and plan contact locations is required. In our work, a humanoid robot is used as a caregiver that is performing a physical assistance task. We propose a method for formulating and initializing a non-linear optimization posture generation problem from an intuitive description of the assistance task and the result of a human point cloud processing. The proposed method allows to plan whole-body posture and contact locations on a task-specific surface of a human body, under robot equilibrium, friction cone, torque/joint limits, collision avoidance, and assistance task inherent constraints. The proposed framework can uniformly handle any arbitrary surface generated from point clouds, for autonomously planing the contact locations and interaction forces on potentially moving, movable, and deformable surfaces, which occur in direct physical human-robot interaction. We conclude the paper with examples of posture generation for physical human-robot interaction scenarios
Assessing Time Series Correlation Significance: A Parametric Approach with Application to Physiological Signals
Correlation coefficients play a pivotal role in quantifying linear
relationships between random variables. Yet, their application to time series
data is very challenging due to temporal dependencies. This paper introduces a
novel approach to estimate the statistical significance of correlation
coefficients in time series data, addressing the limitations of traditional
methods based on the concept of effective degrees of freedom (or effective
sample size, ESS). These effective degrees of freedom represent the independent
sample size that would yield comparable test statistics under the assumption of
no temporal correlation. We propose to assume a parametric Gaussian form for
the autocorrelation function. We show that this assumption, motivated by a
Laplace approximation, enables a simple estimator of the ESS that depends only
on the temporal derivatives of the time series. Through numerical experiments,
we show that the proposed approach yields accurate statistics while
significantly reducing computational overhead. In addition, we evaluate the
adequacy of our approach on real physiological signals, for assessing the
connectivity measures in electrophysiology and detecting correlated arm
movements in motion capture data. Our methodology provides a simple tool for
researchers working with time series data, enabling robust hypothesis testing
in the presence of temporal dependencies.Comment: 14 pages, 8 figure
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