30 research outputs found
On least-cost path for realistic simulation of human motion
We are interested in "human-like" automatic motion simulation with applications in ergonomics.
The apparent redundancy of the humanoid wrt its explicit tasks leads to the problem of choosing a plausible movement in the framework of redundant kinematics.
Some results have been obtained in the human motion literature for reach motion that involves the position of the hands. We discuss these results and a motion generation scheme associated. When orientation is also explicitly required, very few works are available and even the methods for analysis are not defined.
We discuss the choice for metrics adapted to the orientation, and also the problems encountered in defining a proper metric in both position and orientation. Motion capture and simulations are provided in both cases.
The main goals of this paper are: to provide a survey on human motion features at task level for both position and orientation, to propose a kinematic control scheme based on these features, to define properly the error between motion capture and automatic motion simulation
On geodesic paths and least-cost motions for human-like tasks
We are interested in âhuman-likeâ automatic mo- tion generation. The apparent redundancy of the humanoid wrt its explicit tasks lead to the problem of choosing a plausible movement in the framework of redundant kinematics. Some results have been obtained in the human motion literature for reach motion that involves the position of the hands. We discuss these results and a motion generation scheme associated. When orientation is also explicitly required, very few works are available and even the methods for analysis are not defined. We discuss the choice for metrics adapted to the orientation, and also the problems encountered in defining a proper metric in both position and orientation. Motion capture and simulations are provided in both cases. The main goals of this paper are : - to provide a survey on human motion features at task level for both position and orientation,
- to propose a kinematic control scheme based on these features
- to define properly the error between motion capture and automatic motion simulation
On singular values decomposition and patterns for human motion analysis and simulation
We are interested in human motion characterization and automatic motion simulation. The apparent redun- dancy of the humanoid w.r.t its explicit tasks lead to the problem of choosing a plausible movement in the framework of redun- dant kinematics. This work explores the intrinsic relationships between singular value decomposition at kinematic level and optimization principles at task level and joint level. Two task- based schemes devoted to simulation of human motion are then proposed and analyzed. These results are illustrated by motion captures, analyses and task-based simulations. Pattern of singular values serve as a basis for a discussion concerning the similarity of simulated and real motions
A Single-Query Manipulation Planner
In manipulation tasks, a robot interacts with movable object(s). The
configuration space in manipulation planning is thus the Cartesian product of
the configuration space of the robot with those of the movable objects. It is
the complex structure of such a "Composite Configuration Space" that makes
manipulation planning particularly challenging. Previous works approximate the
connectivity of the Composite Configuration Space by means of discretization or
by creating random roadmaps. Such approaches involve an extensive
pre-processing phase, which furthermore has to be re-done each time the
environment changes. In this paper, we propose a high-level Grasp-Placement
Table similar to that proposed by Tournassoud et al. (1987), but which does not
require any discretization or heavy pre-processing. The table captures the
potential connectivity of the Composite Configuration Space while being
specific only to the movable object: in particular, it does not require to be
re-computed when the environment changes. During the query phase, the table is
used to guide a tree-based planner that explores the space systematically. Our
simulations and experiments show that the proposed method enables improvements
in both running time and trajectory quality as compared to existing approaches.Comment: 8 pages, 7 figures, 1 tabl
Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes
Similarly to protein folding, the association of two proteins is driven
by a free energy funnel, determined by favorable interactions in some neighborhood of the
native state. We describe a docking method based on stochastic global minimization of
funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting
for flexibility of the interface side chains. The method, called semi-definite
programming-based underestimation (SDU), employs a general quadratic function to
underestimate a set of local energy minima and uses the resulting underestimator to bias
further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its
application to docking in the rotational and translational space SE(3) is not
straightforward due to the geometry of that space. We introduce a strategy that uses
separate independent variables for side-chain optimization, center-to-center distance of the
two proteins, and five angular descriptors of the relative orientations of the molecules.
The removal of the center-to-center distance turns out to vastly improve the efficiency of
the search, because the five-dimensional space now exhibits a well-behaved energy surface
suitable for underestimation. This algorithm explores the free energy surface spanned by
encounter complexes that correspond to local free energy minima and shows similarity to the
model of macromolecular association that proceeds through a series of collisions. Results
for standard protein docking benchmarks establish that in this space the free energy
landscape is a funnel in a reasonably broad neighborhood of the native state and that the
SDU strategy can generate docking predictions with less than 5 ďż˝ ligand interface Ca
root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared
to Monte Carlo methods
A Certified-Complete Bimanual Manipulation Planner
Planning motions for two robot arms to move an object collaboratively is a
difficult problem, mainly because of the closed-chain constraint, which arises
whenever two robot hands simultaneously grasp a single rigid object. In this
paper, we propose a manipulation planning algorithm to bring an object from an
initial stable placement (position and orientation of the object on the support
surface) towards a goal stable placement. The key specificity of our algorithm
is that it is certified-complete: for a given object and a given environment,
we provide a certificate that the algorithm will find a solution to any
bimanual manipulation query in that environment whenever one exists. Moreover,
the certificate is constructive: at run-time, it can be used to quickly find a
solution to a given query. The algorithm is tested in software and hardware on
a number of large pieces of furniture.Comment: 12 pages, 7 figures, 1 tabl
Encounter complexes and dimensionality reduction in protein-protein association
An outstanding challenge has been to understand the mechanism whereby proteins associate. We report here the results of exhaustively sampling the conformational space in proteinâprotein association using a physics-based energy function. The agreement between experimental intermolecular paramagnetic relaxation enhancement (PRE) data and the PRE profiles calculated from the docked structures shows that the method captures both specific and non-specific encounter complexes. To explore the energy landscape in the vicinity of the native structure, the nonlinear manifold describing the relative orientation of two solid bodies is projected onto a Euclidean space in which the shape of low energy regions is studied by principal component analysis. Results show that the energy surface is canyon-like, with a smooth funnel within a two dimensional subspace capturing over 75% of the total motion. Thus, proteins tend to associate along preferred pathways, similar to sliding of a protein along DNA in the process of protein-DNA recognition