60 research outputs found
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
We describe a learning-based approach to hand-eye coordination for robotic
grasping from monocular images. To learn hand-eye coordination for grasping, we
trained a large convolutional neural network to predict the probability that
task-space motion of the gripper will result in successful grasps, using only
monocular camera images and independently of camera calibration or the current
robot pose. This requires the network to observe the spatial relationship
between the gripper and objects in the scene, thus learning hand-eye
coordination. We then use this network to servo the gripper in real time to
achieve successful grasps. To train our network, we collected over 800,000
grasp attempts over the course of two months, using between 6 and 14 robotic
manipulators at any given time, with differences in camera placement and
hardware. Our experimental evaluation demonstrates that our method achieves
effective real-time control, can successfully grasp novel objects, and corrects
mistakes by continuous servoing.Comment: This is an extended version of "Learning Hand-Eye Coordination for
Robotic Grasping with Large-Scale Data Collection," ISER 2016. Draft modified
to correct typo in Algorithm 1 and add a link to the publicly available
datase
Real-time policy generation and its application to robot grasping
Real time applications such as robotic require real time actions based on the
immediate available data. Machine learning and artificial intelligence rely on
high volume of training informative data set to propose a comprehensive and
useful model for later real time action. Our goal in this paper is to provide a
solution for robot grasping as a real time application without the time and
memory consuming pertaining phase. Grasping as one of the most important
ability of human being is defined as a suitable configuration which depends on
the perceived information from the object. For human being, the best results
obtain when one incorporates the vision data such as the extracted edges and
shape from the object into grasping task. Nevertheless, in robotics, vision
will not suite for every situation. Another possibility to grasping is using
the object shape information from its vicinity. Based on these Haptic
information, similar to human being, one can propose different approaches to
grasping which are called grasping policies. In this work, we are trying to
introduce a real time policy which aims at keeping contact with the object
during movement and alignment on it. First we state problem by system dynamic
equation incorporated by the object constraint surface into dynamic equation.
In next step, the suggested policy to accomplish the task in real time based on
the available sensor information will be presented. The effectiveness of
proposed approach will be evaluated by demonstration results
Improving Data Efficiency of Self-supervised Learning for Robotic Grasping
Given the task of learning robotic grasping solely based on a depth camera
input and gripper force feedback, we derive a learning algorithm from an
applied point of view to significantly reduce the amount of required training
data. Major improvements in time and data efficiency are achieved by: Firstly,
we exploit the geometric consistency between the undistorted depth images and
the task space. Using a relative small, fully-convolutional neural network, we
predict grasp and gripper parameters with great advantages in training as well
as inference performance. Secondly, motivated by the small random grasp success
rate of around 3%, the grasp space was explored in a systematic manner. The
final system was learned with 23000 grasp attempts in around 60h, improving
current solutions by an order of magnitude. For typical bin picking scenarios,
we measured a grasp success rate of 96.6%. Further experiments showed that the
system is able to generalize and transfer knowledge to novel objects and
environments.Comment: Accepted for ICRA 201
A Robotic Auto-Focus System based on Deep Reinforcement Learning
Considering its advantages in dealing with high-dimensional visual input and
learning control policies in discrete domain, Deep Q Network (DQN) could be an
alternative method of traditional auto-focus means in the future. In this
paper, based on Deep Reinforcement Learning, we propose an end-to-end approach
that can learn auto-focus policies from visual input and finish at a clear spot
automatically. We demonstrate that our method - discretizing the action space
with coarse to fine steps and applying DQN is not only a solution to auto-focus
but also a general approach towards vision-based control problems. Separate
phases of training in virtual and real environments are applied to obtain an
effective model. Virtual experiments, which are carried out after the virtual
training phase, indicates that our method could achieve 100% accuracy on a
certain view with different focus range. Further training on real robots could
eliminate the deviation between the simulator and real scenario, leading to
reliable performances in real applications.Comment: To Appear at ICARCV 201
A Learning Framework for Robust Bin Picking by Customized Grippers
Customized grippers have specifically designed fingers to increase the
contact area with the workpieces and improve the grasp robustness. However,
grasp planning for customized grippers is challenging due to the object
variations, surface contacts and structural constraints of the grippers. In
this paper, we propose a learning framework to plan robust grasps for
customized grippers in real-time. The learning framework contains a low-level
optimization-based planner to search for optimal grasps locally under object
shape variations, and a high-level learning-based explorer to learn the grasp
exploration based on previous grasp experience. The optimization-based planner
uses an iterative surface fitting (ISF) to simultaneously search for optimal
gripper transformation and finger displacement by minimizing the surface
fitting error. The high-level learning-based explorer trains a region-based
convolutional neural network (R-CNN) to propose good optimization regions,
which avoids ISF getting stuck in bad local optima and improves the collision
avoidance performance. The proposed learning framework with RCNN-ISF is able to
consider the structural constraints of the gripper, learn grasp exploration
strategy from previous experience, and plan optimal grasps in clutter
environment in real-time. The effectiveness of the algorithm is verified by
experiments.Comment: Submitted to 2019 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2019). arXiv admin note: text overlap with
arXiv:1803.1129
Generating Goal-Directed Visuomotor Plans Based on Learning Using a Predictive Coding-type Deep Visuomotor Recurrent Neural Network Model
The current paper presents how a predictive coding type deep recurrent neural
networks can generate vision-based goal-directed plans based on prior learning
experience by examining experiment results using a real arm robot. The proposed
deep recurrent neural network learns to predict visuo-proprioceptive sequences
by extracting an adequate predictive model from various visuomotor experiences
related to object-directed behaviors. The predictive model was developed in
terms of mapping from intention state space to expected visuo-proprioceptive
sequences space through iterative learning. Our arm robot experiments adopted
with three different tasks with different levels of difficulty showed that the
error minimization principle in the predictive coding framework applied to
inference of the optimal intention states for given goal states can generate
goal-directed plans even for unlearned goal states with generalization. It was,
however, shown that sufficient generalization requires relatively large number
of learning trajectories. The paper discusses possible countermeasure to
overcome this problem.Comment: 6 page
Object Perception and Grasping in Open-Ended Domains
Nowadays service robots are leaving the structured and completely known
environments and entering human-centric settings. For these robots, object
perception and grasping are two challenging tasks due to the high demand for
accurate and real-time responses. Although many problems have already been
understood and solved successfully, many challenges still remain. Open-ended
learning is one of these challenges waiting for many improvements. Cognitive
science revealed that humans learn to recognize object categories and grasp
affordances ceaselessly over time. This ability allows adapting to new
environments by enhancing their knowledge from the accumulation of experiences
and the conceptualization of new object categories. Inspired by this, an
autonomous robot must have the ability to process visual information and
conduct learning and recognition tasks in an open-ended fashion. In this
context, "open-ended" implies that the set of object categories to be learned
is not known in advance, and the training instances are extracted from online
experiences of a robot, and become gradually available over time, rather than
being completely available at the beginning of the learning process.
In my research, I mainly focus on interactive open-ended learning approaches
to recognize multiple objects and their grasp affordances concurrently. In
particular, I try to address the following research questions: (i) What is the
importance of open-ended learning for autonomous robots? (ii) How robots could
learn incrementally from their own experiences as well as from interaction with
humans? (iii) What are the limitations of Deep Learning approaches to be used
in an open-ended manner? (iv) How to evaluate open-ended learning approaches
and what are the right metrics to do so
Learning 6-DoF Grasping and Pick-Place Using Attention Focus
We address a class of manipulation problems where the robot perceives the
scene with a depth sensor and can move its end effector in a space with six
degrees of freedom -- 3D position and orientation. Our approach is to formulate
the problem as a Markov decision process (MDP) with abstract yet generally
applicable state and action representations. Finding a good solution to the MDP
requires adding constraints on the allowed actions. We develop a specific set
of constraints called hierarchical sampling (HSE3S) which causes
the robot to learn a sequence of gazes to focus attention on the task-relevant
parts of the scene. We demonstrate the effectiveness of our approach on three
challenging pick-place tasks (with novel objects in clutter and nontrivial
places) both in simulation and on a real robot, even though all training is
done in simulation
A Critical Investigation of Deep Reinforcement Learning for Navigation
The navigation problem is classically approached in two steps: an exploration
step, where map-information about the environment is gathered; and an
exploitation step, where this information is used to navigate efficiently. Deep
reinforcement learning (DRL) algorithms, alternatively, approach the problem of
navigation in an end-to-end fashion. Inspired by the classical approach, we ask
whether DRL algorithms are able to inherently explore, gather and exploit
map-information over the course of navigation. We build upon Mirowski et al.
[2017] work and introduce a systematic suite of experiments that vary three
parameters: the agent's starting location, the agent's target location, and the
maze structure. We choose evaluation metrics that explicitly measure the
algorithm's ability to gather and exploit map-information. Our experiments show
that when trained and tested on the same maps, the algorithm successfully
gathers and exploits map-information. However, when trained and tested on
different sets of maps, the algorithm fails to transfer the ability to gather
and exploit map-information to unseen maps. Furthermore, we find that when the
goal location is randomized and the map is kept static, the algorithm is able
to gather and exploit map-information but the exploitation is far from optimal.
We open-source our experimental suite in the hopes that it serves as a
framework for the comparison of future algorithms and leads to the discovery of
robust alternatives to classical navigation methods
Robotic Arm Control and Task Training through Deep Reinforcement Learning
This paper proposes a detailed and extensive comparison of the Trust Region
Policy Optimization and DeepQ-Network with Normalized Advantage Functions with
respect to other state of the art algorithms, namely Deep Deterministic Policy
Gradient and Vanilla Policy Gradient. Comparisons demonstrate that the former
have better performances then the latter when asking robotic arms to accomplish
manipulation tasks such as reaching a random target pose and pick &placing an
object. Both simulated and real-world experiments are provided. Simulation lets
us show the procedures that we adopted to precisely estimate the algorithms
hyper-parameters and to correctly design good policies. Real-world experiments
let show that our polices, if correctly trained on simulation, can be
transferred and executed in a real environment with almost no changes.Comment: Submitted to IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS) 201
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