4 research outputs found
Information-theoretic Model Identification and Policy Search using Physics Engines with Application to Robotic Manipulation
We consider the problem of a robot learning the mechanical properties of
objects through physical interaction with the object, and introduce a
practical, data-efficient approach for identifying the motion models of these
objects. The proposed method utilizes a physics engine, where the robot seeks
to identify the inertial and friction parameters of the object by simulating
its motion under different values of the parameters and identifying those that
result in a simulation which matches the observed real motions. The problem is
solved in a Bayesian optimization framework. The same framework is used for
both identifying the model of an object online and searching for a policy that
would minimize a given cost function according to the identified model.
Experimental results both in simulation and using a real robot indicate that
the proposed method outperforms state-of-the-art model-free reinforcement
learning approaches
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video
Pushing is a fundamental robotic skill. Existing work has shown how to
exploit models of pushing to achieve a variety of tasks, including grasping
under uncertainty, in-hand manipulation and clearing clutter. Such models,
however, are approximate, which limits their applicability.
Learning-based methods can reason directly from raw sensory data with
accuracy, and have the potential to generalize to a wider diversity of
scenarios. However, developing and testing such methods requires rich-enough
datasets. In this paper we introduce Omnipush, a dataset with high variety of
planar pushing behavior.
In particular, we provide 250 pushes for each of 250 objects, all recorded
with RGB-D and a high precision tracking system. The objects are constructed so
as to systematically explore key factors that affect pushing --the shape of the
object and its mass distribution-- which have not been broadly explored in
previous datasets, and allow to study generalization in model learning.
Omnipush includes a benchmark for meta-learning dynamic models, which
requires algorithms that make good predictions and estimate their own
uncertainty. We also provide an RGB video prediction benchmark and propose
other relevant tasks that can be suited with this dataset.
Data and code are available at
\url{https://web.mit.edu/mcube/omnipush-dataset/}.Comment: IROS 2019, 8 pages, 7 figure
Let's Push Things Forward: A Survey on Robot Pushing
As robot make their way out of factories into human environments, outer
space, and beyond, they require the skill to manipulate their environment in
multifarious, unforeseeable circumstances. With this regard, pushing is an
essential motion primitive that dramatically extends a robot's manipulation
repertoire. In this work, we review the robotic pushing literature. While
focusing on work concerned with predicting the motion of pushed objects, we
also cover relevant applications of pushing for planning and control. Beginning
with analytical approaches, under which we also subsume physics engines, we
then proceed to discuss work on learning models from data. In doing so, we
dedicate a separate section to deep learning approaches which have seen a
recent upsurge in the literature. Concluding remarks and further research
perspectives are given at the end of the paper
Estimation and Exploitation of Objects' Inertial Parameters in Robotic Grasping and Manipulation: A Survey
Inertial parameters characterise an object's motion under applied forces, and
can provide strong priors for planning and control of robotic actions to
manipulate the object. However, these parameters are not available a-priori in
situations where a robot encounters new objects. In this paper, we describe and
categorise the ways that a robot can identify an object's inertial parameters.
We also discuss grasping and manipulation methods in which knowledge of
inertial parameters is exploited in various ways. We begin with a discussion of
literature which investigates how humans estimate the inertial parameters of
objects, to provide background and motivation for this area of robotics
research. We frame our discussion of the robotics literature in terms of three
categories of estimation methods, according to the amount of interaction with
the object: purely visual, exploratory, and fixed-object. Each category is
analysed and discussed. To demonstrate the usefulness of inertial estimation
research, we describe a number of grasping and manipulation applications that
make use of the inertial parameters of objects. The aim of the paper is to
thoroughly review and categorise existing work in an important, but
under-explored, area of robotics research, present its background and
applications, and suggest future directions. Note that this paper does not
examine methods of identification of the robot's inertial parameters, but
rather the identification of inertial parameters of other objects which the
robot is tasked with manipulating.Comment: To be published in Robotics and Autonomous Systems, Elsevie