9,832 research outputs found
Active vision for dexterous grasping of novel objects
How should a robot direct active vision so as to ensure reliable grasping? We
answer this question for the case of dexterous grasping of unfamiliar objects.
By dexterous grasping we simply mean grasping by any hand with more than two
fingers, such that the robot has some choice about where to place each finger.
Such grasps typically fail in one of two ways, either unmodeled objects in the
scene cause collisions or object reconstruction is insufficient to ensure that
the grasp points provide a stable force closure. These problems can be solved
more easily if active sensing is guided by the anticipated actions. Our
approach has three stages. First, we take a single view and generate candidate
grasps from the resulting partial object reconstruction. Second, we drive the
active vision approach to maximise surface reconstruction quality around the
planned contact points. During this phase, the anticipated grasp is continually
refined. Third, we direct gaze to improve the safety of the planned reach to
grasp trajectory. We show, on a dexterous manipulator with a camera on the
wrist, that our approach (80.4% success rate) outperforms a randomised
algorithm (64.3% success rate).Comment: IROS 2016. Supplementary video: https://youtu.be/uBSOO6tMzw
Robots for Exploration, Digital Preservation and Visualization of Archeological Sites
Monitoring and conservation of archaeological sites
are important activities necessary to prevent damage or to
perform restoration on cultural heritage. Standard techniques,
like mapping and digitizing, are typically used to document the
status of such sites. While these task are normally accomplished
manually by humans, this is not possible when dealing with
hard-to-access areas. For example, due to the possibility of
structural collapses, underground tunnels like catacombs are
considered highly unstable environments. Moreover, they are full
of radioactive gas radon that limits the presence of people only
for few minutes. The progress recently made in the artificial
intelligence and robotics field opened new possibilities for mobile
robots to be used in locations where humans are not allowed
to enter. The ROVINA project aims at developing autonomous
mobile robots to make faster, cheaper and safer the monitoring of
archaeological sites. ROVINA will be evaluated on the catacombs
of Priscilla (in Rome) and S. Gennaro (in Naples)
Visuo-Haptic Grasping of Unknown Objects through Exploration and Learning on Humanoid Robots
Die vorliegende Arbeit befasst sich mit dem Greifen unbekannter Objekte durch humanoide Roboter. Dazu werden visuelle Informationen mit haptischer Exploration kombiniert, um Greifhypothesen zu erzeugen. Basierend auf simulierten Trainingsdaten wird außerdem eine Greifmetrik gelernt, welche die Erfolgswahrscheinlichkeit der Greifhypothesen bewertet und die mit der größten geschätzten Erfolgswahrscheinlichkeit auswählt. Diese wird verwendet, um Objekte mit Hilfe einer reaktiven Kontrollstrategie zu greifen. Die zwei Kernbeiträge der Arbeit sind zum einen die haptische Exploration von unbekannten Objekten und zum anderen das Greifen von unbekannten Objekten mit Hilfe einer neuartigen datengetriebenen Greifmetrik
AcTExplore: Active Tactile Exploration on Unknown Objects
Tactile exploration plays a crucial role in understanding object structures
for fundamental robotics tasks such as grasping and manipulation. However,
efficiently exploring such objects using tactile sensors is challenging,
primarily due to the large-scale unknown environments and limited sensing
coverage of these sensors. To this end, we present AcTExplore, an active
tactile exploration method driven by reinforcement learning for object
reconstruction at scales that automatically explores the object surfaces in a
limited number of steps. Through sufficient exploration, our algorithm
incrementally collects tactile data and reconstructs 3D shapes of the objects
as well, which can serve as a representation for higher-level downstream tasks.
Our method achieves an average of 95.97% IoU coverage on unseen YCB objects
while just being trained on primitive shapes. Project Webpage:
https://prg.cs.umdedu/AcTExploreComment: 8 pages, 6 figure
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
3D Shape Perception from Monocular Vision, Touch, and Shape Priors
Perceiving accurate 3D object shape is important for robots to interact with
the physical world. Current research along this direction has been primarily
relying on visual observations. Vision, however useful, has inherent
limitations due to occlusions and the 2D-3D ambiguities, especially for
perception with a monocular camera. In contrast, touch gets precise local shape
information, though its efficiency for reconstructing the entire shape could be
low. In this paper, we propose a novel paradigm that efficiently perceives
accurate 3D object shape by incorporating visual and tactile observations, as
well as prior knowledge of common object shapes learned from large-scale shape
repositories. We use vision first, applying neural networks with learned shape
priors to predict an object's 3D shape from a single-view color image. We then
use tactile sensing to refine the shape; the robot actively touches the object
regions where the visual prediction has high uncertainty. Our method
efficiently builds the 3D shape of common objects from a color image and a
small number of tactile explorations (around 10). Our setup is easy to apply
and has potentials to help robots better perform grasping or manipulation tasks
on real-world objects.Comment: IROS 2018. The first two authors contributed equally to this wor
Dexterous manipulation of unknown objects using virtual contact points
The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.Peer ReviewedPostprint (author's final draft
In-Hand Manipulation of Unknown Objects with Tactile Sensing for Insertion
In this paper, we present a method to manipulate unknown objects in-hand
using tactile sensing without relying on a known object model. In many cases,
vision-only approaches may not be feasible; for example, due to occlusion in
cluttered spaces. We address this limitation by introducing a method to
reorient unknown objects using tactile sensing. It incrementally builds a
probabilistic estimate of the object shape and pose during task-driven
manipulation. Our approach uses Bayesian optimization to balance exploration of
the global object shape with efficient task completion. To demonstrate the
effectiveness of our method, we apply it to a simulated Tactile-Enabled Roller
Grasper, a gripper that rolls objects in hand while collecting tactile data. We
evaluate our method on an insertion task with randomly generated objects and
find that it reliably reorients objects while significantly reducing the
exploration time
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