1,968 research outputs found
Soft Finger Model with Adaptive Contact Geometry for Grasping and Manipulation Tasks
This paper presents a method for building analytical contact models for soft fingers. Friction constraints are derived based on general expressions for non-planar contacts of elastic bodies, taking into account the local geometry and structure of the objects in contact. These constraints are then formulated as a linear complementarity problem, the solution of which provides the normal and frictional forces applied at each contact, as well as the relative velocity of the bodies involved. This approach captures frictional effects such as coupling between tangential force and frictional torque. We illustrate this method by analyzing manipulation tasks performed by an anthropomorphic robotic hand equipped with soft fingerpads
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
Proprioceptive Learning with Soft Polyhedral Networks
Proprioception is the "sixth sense" that detects limb postures with motor
neurons. It requires a natural integration between the musculoskeletal systems
and sensory receptors, which is challenging among modern robots that aim for
lightweight, adaptive, and sensitive designs at a low cost. Here, we present
the Soft Polyhedral Network with an embedded vision for physical interactions,
capable of adaptive kinesthesia and viscoelastic proprioception by learning
kinetic features. This design enables passive adaptations to omni-directional
interactions, visually captured by a miniature high-speed motion tracking
system embedded inside for proprioceptive learning. The results show that the
soft network can infer real-time 6D forces and torques with accuracies of
0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also
incorporate viscoelasticity in proprioception during static adaptation by
adding a creep and relaxation modifier to refine the predicted results. The
proposed soft network combines simplicity in design, omni-adaptation, and
proprioceptive sensing with high accuracy, making it a versatile solution for
robotics at a low cost with more than 1 million use cycles for tasks such as
sensitive and competitive grasping, and touch-based geometry reconstruction.
This study offers new insights into vision-based proprioception for soft robots
in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International
Journal of Robotics Research for revie
Innovative robot hand designs of reduced complexity for dexterous manipulation
This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects.
Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension.
Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty.
In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness.
Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm.
In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces
SeeThruFinger: See and Grasp Anything with a Soft Touch
We present SeeThruFinger, a soft robotic finger with an in-finger vision for
multi-modal perception, including visual perception and tactile sensing, for
geometrically adaptive and real-time reactive grasping. Multi-modal perception
of intrinsic and extrinsic interactions is critical in building intelligent
robots that learn. Instead of adding various sensors for different modalities,
a preferred solution is to integrate them into one elegant and coherent design,
which is a challenging task. This study leverages the Soft Polyhedral Network
design as a robotic finger, capable of omni-directional adaptation with an
unobstructed view of the finger's spatial deformation from the inside. By
embedding a miniature camera underneath, we achieve the visual perception of
the external environment by inpainting the finger mask using E2FGV, which can
be used for object detection in the downstream tasks for grasping. After
contacting the objects, we use real-time object segmentation algorithms, such
as XMem, to track the soft finger's spatial deformations. We also learned a
Supervised Variational Autoencoder to enable tactile sensing of 6D forces and
torques for reactive grasp. As a result, we achieved multi-modal perception,
including visual perception and tactile sensing, and soft, adaptive object
grasping within a single vision-based soft finger design compatible with
multi-fingered robotic grippers.Comment: 10 pages, 5 figures, 1 table, submitted to Soft Robotics under revie
Improved GelSight Tactile Sensor for Measuring Geometry and Slip
A GelSight sensor uses an elastomeric slab covered with a reflective membrane
to measure tactile signals. It measures the 3D geometry and contact force
information with high spacial resolution, and successfully helped many
challenging robot tasks. A previous sensor, based on a semi-specular membrane,
produces high resolution but with limited geometry accuracy. In this paper, we
describe a new design of GelSight for robot gripper, using a Lambertian
membrane and new illumination system, which gives greatly improved geometric
accuracy while retaining the compact size. We demonstrate its use in measuring
surface normals and reconstructing height maps using photometric stereo. We
also use it for the task of slip detection, using a combination of information
about relative motions on the membrane surface and the shear distortions. Using
a robotic arm and a set of 37 everyday objects with varied properties, we find
that the sensor can detect translational and rotational slip in general cases,
and can be used to improve the stability of the grasp.Comment: IEEE/RSJ International Conference on Intelligent Robots and System
Advancing the Underactuated Grasping Capabilities of Single Actuator Prosthetic Hands
The last decade has seen significant advancements in upper limb prosthetics, specifically in the myoelectric control and powered prosthetic hand fields, leading to more active and social lifestyles for the upper limb amputee community. Notwithstanding the improvements in complexity and control of myoelectric prosthetic hands, grasping still remains one of the greatest challenges in robotics. Upper-limb amputees continue to prefer more antiquated body-powered or powered hook terminal devices that are favored for their control simplicity, lightweight and low cost; however, these devices are nominally unsightly and lack in grasp variety. The varying drawbacks of both complex myoelectric and simple body-powered devices have led to low adoption rates for all upper limb prostheses by amputees, which includes 35% pediatric and 23% adult rejection for complex devices and 45% pediatric and 26% adult rejection for body-powered devices [1]. My research focuses on progressing the grasping capabilities of prosthetic hands driven by simple control and a single motor, to combine the dexterous functionality of the more complex hands with the intuitive control of the more simplistic body-powered devices with the goal of helping upper limb amputees return to more active and social lifestyles. Optimization of a prosthetic hand driven by a single actuator requires the optimization of many facets of the hand. This includes optimization of the finger kinematics, underactuated mechanisms, geometry, materials and performance when completing activities of daily living. In my dissertation, I will present chapters dedicated to improving these subsystems of single actuator prosthetic hands to better replicate human hand function from simple control. First, I will present a framework created to optimize precision grasping – which is nominally unstable in underactuated configurations – from a single actuator. I will then present several novel mechanisms that allow a single actuator to map to higher degree of freedom motion and multiple commonly used grasp types. I will then discuss how fingerpad geometry and materials can better grasp acquisition and frictional properties within the hand while also providing a method of fabricating lightweight custom prostheses. Last, I will analyze the results of several human subject testing studies to evaluate the optimized hands performance on activities of daily living and compared to other commercially available prosthesis
Advanced grasping with the Pisa/IIT softHand
This chapter presents the hardware, software and overall strategy used by the team UNIPI-IIT-QB to participate to the Robotic Grasping and Manipulation Competition. It relies on the PISA/IIT SoftHand, which is underactuated soft robotic hand that can adapt to the grasped object shape and is compliant with the environment. It was used for the hand-in-hand and for the simulation tracks, where the team reached first and third places respectively
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