5,155 research outputs found
GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger
This work describes the development of a high-resolution tactile-sensing
finger for robot grasping. This finger, inspired by previous GelSight sensing
techniques, features an integration that is slimmer, more robust, and with more
homogeneous output than previous vision-based tactile sensors. To achieve a
compact integration, we redesign the optical path from illumination source to
camera by combining light guides and an arrangement of mirror reflections. We
parameterize the optical path with geometric design variables and describe the
tradeoffs between the finger thickness, the depth of field of the camera, and
the size of the tactile sensing area. The sensor sustains the wear from
continuous use -- and abuse -- in grasping tasks by combining tougher materials
for the compliant soft gel, a textured fabric skin, a structurally rigid body,
and a calibration process that maintains homogeneous illumination and contrast
of the tactile images during use. Finally, we evaluate the sensor's durability
along four metrics that track the signal quality during more than 3000 grasping
experiments.Comment: RA-L Pre-print. 8 page
Shear-invariant Sliding Contact Perception with a Soft Tactile Sensor
Manipulation tasks often require robots to be continuously in contact with an
object. Therefore tactile perception systems need to handle continuous contact
data. Shear deformation causes the tactile sensor to output path-dependent
readings in contrast to discrete contact readings. As such, in some
continuous-contact tasks, sliding can be regarded as a disturbance over the
sensor signal. Here we present a shear-invariant perception method based on
principal component analysis (PCA) which outputs the required information about
the environment despite sliding motion. A compliant tactile sensor (the TacTip)
is used to investigate continuous tactile contact. First, we evaluate the
method offline using test data collected whilst the sensor slides over an edge.
Then, the method is used within a contour-following task applied to 6 objects
with varying curvatures; all contours are successfully traced. The method
demonstrates generalisation capabilities and could underlie a more
sophisticated controller for challenging manipulation or exploration tasks in
unstructured environments. A video showing the work described in the paper can
be found at https://youtu.be/wrTM61-pieUComment: Accepted in ICRA 201
Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing
This work deals with a practical everyday problem: stable object placement on
flat surfaces starting from unknown initial poses. Common object-placing
approaches require either complete scene specifications or extrinsic sensor
measurements, e.g., cameras, that occasionally suffer from occlusions. We
propose a novel approach for stable object placing that combines tactile
feedback and proprioceptive sensing. We devise a neural architecture that
estimates a rotation matrix, resulting in a corrective gripper movement that
aligns the object with the placing surface for the subsequent object
manipulation. We compare models with different sensing modalities, such as
force-torque and an external motion capture system, in real-world object
placing tasks with different objects. The experimental evaluation of our
placing policies with a set of unseen everyday objects reveals significant
generalization of our proposed pipeline, suggesting that tactile sensing plays
a vital role in the intrinsic understanding of robotic dexterous object
manipulation. Code, models, and supplementary videos are available at
https://sites.google.com/view/placing-by-touching
Robot Composite Learning and the Nunchaku Flipping Challenge
Advanced motor skills are essential for robots to physically coexist with
humans. Much research on robot dynamics and control has achieved success on
hyper robot motor capabilities, but mostly through heavily case-specific
engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous
manner, robot learning from human demonstration (LfD) has achieved great
progress, but still has limitations handling dynamic skills and compound
actions. In this paper, we present a composite learning scheme which goes
beyond LfD and integrates robot learning from human definition, demonstration,
and evaluation. The method tackles advanced motor skills that require dynamic
time-critical maneuver, complex contact control, and handling partly soft
partly rigid objects. We also introduce the "nunchaku flipping challenge", an
extreme test that puts hard requirements to all these three aspects. Continued
from our previous presentations, this paper introduces the latest update of the
composite learning scheme and the physical success of the nunchaku flipping
challenge
Pose-Based Tactile Servoing: Controlled Soft Touch using Deep Learning
This article describes a new way of controlling robots using soft tactile
sensors: pose-based tactile servo (PBTS) control. The basic idea is to embed a
tactile perception model for estimating the sensor pose within a servo control
loop that is applied to local object features such as edges and surfaces. PBTS
control is implemented with a soft curved optical tactile sensor (the BRL
TacTip) using a convolutional neural network trained to be insensitive to
shear. In consequence, robust and accurate controlled motion over various
complex 3D objects is attained. First, we review tactile servoing and its
relation to visual servoing, before formalising PBTS control. Then, we assess
tactile servoing over a range of regular and irregular objects. Finally, we
reflect on the relation to visual servo control and discuss how controlled soft
touch gives a route towards human-like dexterity in robots.Comment: A summary video is available here https://youtu.be/12-DJeRcfn0 *NL
and JL contributed equally to this wor
Object Recognition and Localization : the Role of Tactile Sensors
Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This thesis presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Sequential Filter (BRICPSF) is based on an innovative combination of a sequential filter, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in simulation and using actual hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses BRICPSF for object part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments
Hybrid Architectures for Object Pose and Velocity Tracking at the Intersection of Kalman Filtering and Machine Learning
The study of object perception algorithms is fundamental for the development of robotic platforms capable of planning and executing actions involving objects with high precision, reliability and safety. Indeed, this topic has been vastly explored in both the robotic and computer vision research communities using diverse techniques, ranging from classical Bayesian filtering to more modern Machine Learning techniques, and complementary sensing modalities such as vision and touch. Recently, the ever-growing availability of tools for synthetic data generation has substantially increased the adoption of Deep Learning for both 2D tasks, as object detection and segmentation, and 6D tasks, such as object pose estimation and tracking.
The proposed methods exhibit interesting performance on computer vision benchmarks and robotic tasks, e.g. using object pose estimation for grasp planning purposes. Nonetheless, they generally do not consider useful information connected with the physics of the object motion and the peculiarities and requirements of robotic systems. Examples are the necessity to provide well-behaved output signals for robot motion control, the possibility to integrate modelling priors on the motion of the object and algorithmic priors. These help exploit the temporal correlation of the object poses, handle the pose uncertainties and mitigate the effect of outliers. Most of these concepts are considered in classical approaches, e.g. from the Bayesian and Kalman filtering literature, which however are not as powerful as Deep Learning in handling visual data. As a consequence, the development of hybrid architectures that combine the best features from both worlds is particularly appealing in a robotic setting.
Motivated by these considerations, in this Thesis, I aimed at devising hybrid architectures for object perception, focusing on the task of object pose and velocity tracking. The proposed architectures use Kalman filtering supported by state-of-the-art Deep Neural Networks to track the 6D pose and velocity of objects from images. The devised solutions exhibit state-of-the-art performance, increased modularity and do not require training to implement the actual tracking behaviors. Furthermore, they can track even fast object motions despite the possible non-negligible inference times of the adopted neural networks. Also, by relying on data-driven Kalman filtering, I explored a paradigm that enables to track the state of systems that cannot be easily modeled analytically. Specifically, I used this approach to learn the measurement model of soft 3D tactile sensors and address the problem of tracking the sliding motion of hand-held objects
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