20 research outputs found
RoboPlanner: Towards an Autonomous Robotic Action Planning Framework for Industry 4.0
Autonomous robots are being increasingly integrated into manufacturing, supply chain and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex Industry 4.0 tasks, the autonomous robots require robust action plans, that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases, that may be queried during planning and execution. In this paper, we propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, we model the knowledge of world models, robotic capabilities and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning. At runtime, integrity constraints on world model observations are used to update knowledge bases. We demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses
MORE: Simultaneous Multi-View 3D Object Recognition and Pose Estimation
Simultaneous object recognition and pose estimation are two key
functionalities for robots to safely interact with humans as well as
environments. Although both object recognition and pose estimation use visual
input, most state-of-the-art tackles them as two separate problems since the
former needs a view-invariant representation while object pose estimation
necessitates a view-dependent description. Nowadays, multi-view Convolutional
Neural Network (MVCNN) approaches show state-of-the-art classification
performance. Although MVCNN object recognition has been widely explored, there
has been very little research on multi-view object pose estimation methods, and
even less on addressing these two problems simultaneously. The pose of virtual
cameras in MVCNN methods is often pre-defined in advance, leading to bound the
application of such approaches. In this paper, we propose an approach capable
of handling object recognition and pose estimation simultaneously. In
particular, we develop a deep object-agnostic entropy estimation model, capable
of predicting the best viewpoints of a given 3D object. The obtained views of
the object are then fed to the network to simultaneously predict the pose and
category label of the target object. Experimental results showed that the views
obtained from such positions are descriptive enough to achieve a good accuracy
score. Code is available online at: https://github.com/tparisotto/more_mvcn
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains
A robot working in human-centric environments needs to know which kind of
objects exist in the scene, where they are, and how to grasp and manipulate
various objects in different situations to help humans in everyday tasks.
Therefore, object recognition and grasping are two key functionalities for such
robots. Most state-of-the-art tackles object recognition and grasping as two
separate problems while both use visual input. Furthermore, the knowledge of
the robot is fixed after the training phase. In such cases, if the robot faces
new object categories, it must retrain from scratch to incorporate new
information without catastrophic interference. To address this problem, we
propose a deep learning architecture with augmented memory capacities to handle
open-ended object recognition and grasping simultaneously. In particular, our
approach takes multi-views of an object as input and jointly estimates
pixel-wise grasp configuration as well as a deep scale- and rotation-invariant
representation as outputs. The obtained representation is then used for
open-ended object recognition through a meta-active learning technique. We
demonstrate the ability of our approach to grasp never-seen-before objects and
to rapidly learn new object categories using very few examples on-site in both
simulation and real-world settings.Comment: arXiv admin note: text overlap with arXiv:2103.1099
The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation
Service robots are appearing more and more in our daily life. The development
of service robots combines multiple fields of research, from object perception
to object manipulation. The state-of-the-art continues to improve to make a
proper coupling between object perception and manipulation. This coupling is
necessary for service robots not only to perform various tasks in a reasonable
amount of time but also to continually adapt to new environments and safely
interact with non-expert human users. Nowadays, robots are able to recognize
various objects, and quickly plan a collision-free trajectory to grasp a target
object in predefined settings. Besides, in most of the cases, there is a
reliance on large amounts of training data. Therefore, the knowledge of such
robots is fixed after the training phase, and any changes in the environment
require complicated, time-consuming, and expensive robot re-programming by
human experts. Therefore, these approaches are still too rigid for real-life
applications in unstructured environments, where a significant portion of the
environment is unknown and cannot be directly sensed or controlled. In such
environments, no matter how extensive the training data used for batch
learning, a robot will always face new objects. Therefore, apart from batch
learning, the robot should be able to continually learn about new object
categories and grasp affordances from very few training examples on-site.
Moreover, apart from robot self-learning, non-expert users could interactively
guide the process of experience acquisition by teaching new concepts, or by
correcting insufficient or erroneous concepts. In this way, the robot will
constantly learn how to help humans in everyday tasks by gaining more and more
experiences without the need for re-programming