12,874 research outputs found
More than a Million Ways to Be Pushed: A High-Fidelity Experimental Dataset of Planar Pushing
Pushing is a motion primitive useful to handle objects that are too large,
too heavy, or too cluttered to be grasped. It is at the core of much of robotic
manipulation, in particular when physical interaction is involved. It seems
reasonable then to wish for robots to understand how pushed objects move.
In reality, however, robots often rely on approximations which yield models
that are computable, but also restricted and inaccurate. Just how close are
those models? How reasonable are the assumptions they are based on? To help
answer these questions, and to get a better experimental understanding of
pushing, we present a comprehensive and high-fidelity dataset of planar pushing
experiments. The dataset contains timestamped poses of a circular pusher and a
pushed object, as well as forces at the interaction.We vary the push
interaction in 6 dimensions: surface material, shape of the pushed object,
contact position, pushing direction, pushing speed, and pushing acceleration.
An industrial robot automates the data capturing along precisely controlled
position-velocity-acceleration trajectories of the pusher, which give dense
samples of positions and forces of uniform quality.
We finish the paper by characterizing the variability of friction, and
evaluating the most common assumptions and simplifications made by models of
frictional pushing in robotics.Comment: 8 pages, 10 figure
Friction Variability in Planar Pushing Data: Anisotropic Friction and Data-collection Bias
Friction plays a key role in manipulating objects. Most of what we do with
our hands, and most of what robots do with their grippers, is based on the
ability to control frictional forces. This paper aims to better understand the
variability and predictability of planar friction. In particular, we focus on
the analysis of a recent dataset on planar pushing by Yu et al. [1] devised to
create a data-driven footprint of planar friction.
We show in this paper how we can explain a significant fraction of the
observed unconventional phenomena, e.g., stochasticity and multi-modality, by
combining the effects of material non-homogeneity, anisotropy of friction and
biases due to data collection dynamics, hinting that the variability is
explainable but inevitable in practice.
We introduce an anisotropic friction model and conduct simulation experiments
comparing with more standard isotropic friction models. The anisotropic
friction between object and supporting surface results in convergence of
initial condition during the automated data collection. Numerical results
confirm that the anisotropic friction model explains the bias in the dataset
and the apparent stochasticity in the outcome of a push. The fact that the data
collection process itself can originate biases in the collected datasets,
resulting in deterioration of trained models, calls attention to the data
collection dynamics.Comment: 8 pages, 13 figure
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
This work studies the problem of stochastic dynamic filtering and state
propagation with complex beliefs. The main contribution is GP-SUM, a filtering
algorithm tailored to dynamic systems and observation models expressed as
Gaussian Processes (GP), and to states represented as a weighted sum of
Gaussians. The key attribute of GP-SUM is that it does not rely on
linearizations of the dynamic or observation models, or on unimodal Gaussian
approximations of the belief, hence enables tracking complex state
distributions. The algorithm can be seen as a combination of a sampling-based
filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by
sampling the state distribution and propagating each sample through the dynamic
system and observation models. On the other hand, it achieves effective
sampling and accurate probabilistic propagation by relying on the GP form of
the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM
outperforms several GP-Bayes and Particle Filters on a standard benchmark. We
also demonstrate its use in a pushing task, predicting with experimental
accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure
A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application
We propose a polynomial force-motion model for planar sliding. The set of
generalized friction loads is the 1-sublevel set of a polynomial whose gradient
directions correspond to generalized velocities. Additionally, the polynomial
is confined to be convex even-degree homogeneous in order to obey the maximum
work inequality, symmetry, shape invariance in scale, and fast invertibility.
We present a simple and statistically-efficient model identification procedure
using a sum-of-squares convex relaxation. Simulation and robotic experiments
validate the accuracy and efficiency of our approach. We also show practical
applications of our model including stable pushing of objects and free sliding
dynamic simulations.Comment: 2016 IEEE International Conference on Robotics and Automation (ICRA
Particle-scale structure in frozen colloidal suspensions from small angle X-ray scattering
During directional solidification of the solvent in a colloidal suspension, the colloidal particles segregate from the growing solid, forming high-particle-density regions with structure on a hierarchy of length scales ranging from that of the particle-scale packing to the large-scale spacing between these regions. Previous work has mostly concentrated on the medium- to large-length scale structure, as it is the most accessible and thought to be more technologically relevant. However, the packing of the colloids at the particle-scale is an important component not only in theoretical descriptions of the segregation process, but also to the utility of freeze-cast materials for new applications. Here we present the results of experiments in which we investigated this structure across a wide range of length scales using a combination of small angle X-ray scattering and direct optical imaging. As expected, during freezing the particles were concentrated into regions between ice dendrites forming a microscopic pattern of high- and low-particle-density regions. X-ray scattering indicates that the particles in the high density regions were so closely packed as to be touching. However, the arrangement of the particles does not conform to that predicted by any standard inter-particle pair potentials, suggesting that the particle packing induced by freezing differs from that formed during equilibrium or steady-state densification processes
Directed assembly of optically bound matter
We present a study of optically bound matter formation in a counter-propagating evanescent field, exploiting total internal reflection on a prism surface. Small ensembles of silica microspheres are assembled in a controlled manner using optical tweezers. The structures and dynamics of the resulting optically bound chains are interpreted using a simulation implementing generalized Lorentz-Mie theory. In particular, we observe enhancement of the scattering force along the propagation direction of the optically bound colloidal chains leading to a microscopic analogue of a driven pendulum which, at least superficially, resembles Newton’s cradle
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