86 research outputs found
A probabilistic data-driven model for planar pushing
This paper presents a data-driven approach to model planar pushing
interaction to predict both the most likely outcome of a push and its expected
variability. The learned models rely on a variation of Gaussian processes with
input-dependent noise called Variational Heteroscedastic Gaussian processes
(VHGP) that capture the mean and variance of a stochastic function. We show
that we can learn accurate models that outperform analytical models after less
than 100 samples and saturate in performance with less than 1000 samples. We
validate the results against a collected dataset of repeated trajectories, and
use the learned models to study questions such as the nature of the variability
in pushing, and the validity of the quasi-static assumption.Comment: 8 pages, 11 figures, ICRA 201
Tactile Mapping and Localization from High-Resolution Tactile Imprints
This work studies the problem of shape reconstruction and object localization
using a vision-based tactile sensor, GelSlim. The main contributions are the
recovery of local shapes from contact, an approach to reconstruct the tactile
shape of objects from tactile imprints, and an accurate method for object
localization of previously reconstructed objects. The algorithms can be applied
to a large variety of 3D objects and provide accurate tactile feedback for
in-hand manipulation. Results show that by exploiting the dense tactile
information we can reconstruct the shape of objects with high accuracy and do
on-line object identification and localization, opening the door to reactive
manipulation guided by tactile sensing. We provide videos and supplemental
information in the project's website
http://web.mit.edu/mcube/research/tactile_localization.html.Comment: ICRA 2019, 7 pages, 7 figures. Website:
http://web.mit.edu/mcube/research/tactile_localization.html Video:
https://youtu.be/uMkspjmDbq
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
FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback
In this paper, we address the problem of using visuo-tactile feedback for
6-DoF localization and 3D reconstruction of unknown in-hand objects. We propose
FingerSLAM, a closed-loop factor graph-based pose estimator that combines local
tactile sensing at finger-tip and global vision sensing from a wrist-mount
camera. FingerSLAM is constructed with two constituent pose estimators: a
multi-pass refined tactile-based pose estimator that captures movements from
detailed local textures, and a single-pass vision-based pose estimator that
predicts from a global view of the object. We also design a loop closure
mechanism that actively matches current vision and tactile images to previously
stored key-frames to reduce accumulated error. FingerSLAM incorporates the two
sensing modalities of tactile and vision, as well as the loop closure mechanism
with a factor graph-based optimization framework. Such a framework produces an
optimized pose estimation solution that is more accurate than the standalone
estimators. The estimated poses are then used to reconstruct the shape of the
unknown object incrementally by stitching the local point clouds recovered from
tactile images. We train our system on real-world data collected with 20
objects. We demonstrate reliable visuo-tactile pose estimation and shape
reconstruction through quantitative and qualitative real-world evaluations on 6
objects that are unseen during training.Comment: Submitted and accepted to 2023 IEEE International Conference on
Robotics and Automation (ICRA 2023
Graph Element Networks: adaptive, structured computation and memory
We explore the use of graph neural networks (GNNs) to model spatial processes
in which there is no a priori graphical structure. Similar to finite element
analysis, we assign nodes of a GNN to spatial locations and use a computational
process defined on the graph to model the relationship between an initial
function defined over a space and a resulting function in the same space. We
use GNNs as a computational substrate, and show that the locations of the nodes
in space as well as their connectivity can be optimized to focus on the most
complex parts of the space. Moreover, this representational strategy allows the
learned input-output relationship to generalize over the size of the underlying
space and run the same model at different levels of precision, trading
computation for accuracy. We demonstrate this method on a traditional PDE
problem, a physical prediction problem from robotics, and learning to predict
scene images from novel viewpoints.Comment: Accepted to ICML 201
Combining Physical Simulators and Object-Based Networks for Control
Physics engines play an important role in robot planning and control;
however, many real-world control problems involve complex contact dynamics that
cannot be characterized analytically. Most physics engines therefore employ .
approximations that lead to a loss in precision. In this paper, we propose a
hybrid dynamics model, simulator-augmented interaction networks (SAIN),
combining a physics engine with an object-based neural network for dynamics
modeling. Compared with existing models that are purely analytical or purely
data-driven, our hybrid model captures the dynamics of interacting objects in a
more accurate and data-efficient manner.Experiments both in simulation and on a
real robot suggest that it also leads to better performance when used in
complex control tasks. Finally, we show that our model generalizes to novel
environments with varying object shapes and materials.Comment: ICRA 2019; Project page: http://sain.csail.mit.ed
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