29 research outputs found
Reactive Planar Manipulation with Convex Hybrid MPC
This paper presents a reactive controller for planar manipulation tasks that
leverages machine learning to achieve real-time performance. The approach is
based on a Model Predictive Control (MPC) formulation, where the goal is to
find an optimal sequence of robot motions to achieve a desired object motion.
Due to the multiple contact modes associated with frictional interactions, the
resulting optimization program suffers from combinatorial complexity when
tasked with determining the optimal sequence of modes.
To overcome this difficulty, we formulate the search for the optimal mode
sequences offline, separately from the search for optimal control inputs
online. Using tools from machine learning, this leads to a convex hybrid MPC
program that can be solved in real-time. We validate our algorithm on a planar
manipulation experimental setup where results show that the convex hybrid MPC
formulation with learned modes achieves good closed-loop performance on a
trajectory tracking problem
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
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
Realtime State Estimation with Tactile and Visual sensing. Application to Planar Manipulation
Accurate and robust object state estimation enables successful object
manipulation. Visual sensing is widely used to estimate object poses. However,
in a cluttered scene or in a tight workspace, the robot's end-effector often
occludes the object from the visual sensor. The robot then loses visual
feedback and must fall back on open-loop execution.
In this paper, we integrate both tactile and visual input using a framework
for solving the SLAM problem, incremental smoothing and mapping (iSAM), to
provide a fast and flexible solution. Visual sensing provides global pose
information but is noisy in general, whereas contact sensing is local, but its
measurements are more accurate relative to the end-effector. By combining them,
we aim to exploit their advantages and overcome their limitations. We explore
the technique in the context of a pusher-slider system. We adapt iSAM's
measurement cost and motion cost to the pushing scenario, and use an
instrumented setup to evaluate the estimation quality with different object
shapes, on different surface materials, and under different contact modes
Real-Time Online Re-Planning for Grasping Under Clutter and Uncertainty
We consider the problem of grasping in clutter. While there have been motion
planners developed to address this problem in recent years, these planners are
mostly tailored for open-loop execution. Open-loop execution in this domain,
however, is likely to fail, since it is not possible to model the dynamics of
the multi-body multi-contact physical system with enough accuracy, neither is
it reasonable to expect robots to know the exact physical properties of
objects, such as frictional, inertial, and geometrical. Therefore, we propose
an online re-planning approach for grasping through clutter. The main challenge
is the long planning times this domain requires, which makes fast re-planning
and fluent execution difficult to realize. In order to address this, we propose
an easily parallelizable stochastic trajectory optimization based algorithm
that generates a sequence of optimal controls. We show that by running this
optimizer only for a small number of iterations, it is possible to perform real
time re-planning cycles to achieve reactive manipulation under clutter and
uncertainty.Comment: Published as a conference paper in IEEE Humanoids 201