460 research outputs found
Assistive robotics: research challenges and ethics education initiatives
Assistive robotics is a fast growing field aimed at helping healthcarers in hospitals, rehabilitation centers and nursery homes, as well as empowering people with reduced mobility at home, so that they can autonomously fulfill their daily living activities. The need to function in dynamic human-centered environments poses new research challenges: robotic assistants need to have friendly interfaces, be highly adaptable and customizable, very compliant and intrinsically safe to people, as well as able to handle deformable materials.
Besides technical challenges, assistive robotics raises also ethical defies, which have led to the emergence of a new discipline: Roboethics. Several institutions are developing regulations and standards, and many ethics education initiatives include contents on human-robot interaction and human dignity in assistive situations.
In this paper, the state of the art in assistive robotics is briefly reviewed, and educational materials from a university course on Ethics in Social Robotics and AI focusing on the assistive context are presented.Peer ReviewedPostprint (author's final draft
Data-driven robotic manipulation of cloth-like deformable objects : the present, challenges and future prospects
Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms.Publisher PDFPeer reviewe
Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary
The complex physical properties of highly deformable materials such as
clothes pose significant challenges fanipulation systems. We present a novel
visual feedback dictionary-based method for manipulating defoor autonomous
robotic mrmable objects towards a desired configuration. Our approach is based
on visual servoing and we use an efficient technique to extract key features
from the RGB sensor stream in the form of a histogram of deformable model
features. These histogram features serve as high-level representations of the
state of the deformable material. Next, we collect manipulation data and use a
visual feedback dictionary that maps the velocity in the high-dimensional
feature space to the velocity of the robotic end-effectors for manipulation. We
have evaluated our approach on a set of complex manipulation tasks and
human-robot manipulation tasks on different cloth pieces with varying material
characteristics.Comment: The video is available at goo.gl/mDSC4
Robotic Ironing with 3D Perception and Force/Torque Feedback in Household Environments
As robotic systems become more popular in household environments, the
complexity of required tasks also increases. In this work we focus on a
domestic chore deemed dull by a majority of the population, the task of
ironing. The presented algorithm improves on the limited number of previous
works by joining 3D perception with force/torque sensing, with emphasis on
finding a practical solution with a feasible implementation in a domestic
setting. Our algorithm obtains a point cloud representation of the working
environment. From this point cloud, the garment is segmented and a custom
Wrinkleness Local Descriptor (WiLD) is computed to determine the location of
the present wrinkles. Using this descriptor, the most suitable ironing path is
computed and, based on it, the manipulation algorithm performs the
force-controlled ironing operation. Experiments have been performed with a
humanoid robot platform, proving that our algorithm is able to detect
successfully wrinkles present in garments and iteratively reduce the
wrinkleness using an unmodified iron.Comment: Accepted and to be published on the 2017 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2017) that will be held in
Vancouver, Canada, September 24-28, 201
Elastic Context: Encoding Elasticity for Data-driven Models of Textiles
Physical interaction with textiles, such as assistive dressing, relies on
advanced dextreous capabilities. The underlying complexity in textile behavior
when being pulled and stretched, is due to both the yarn material properties
and the textile construction technique. Today, there are no commonly adopted
and annotated datasets on which the various interaction or property
identification methods are assessed. One important property that affects the
interaction is material elasticity that results from both the yarn material and
construction technique: these two are intertwined and, if not known a-priori,
almost impossible to identify through sensing commonly available on robotic
platforms. We introduce Elastic Context (EC), a concept that integrates various
properties that affect elastic behavior, to enable a more effective physical
interaction with textiles. The definition of EC relies on stress/strain curves
commonly used in textile engineering, which we reformulated for robotic
applications. We employ EC using Graph Neural Network (GNN) to learn
generalized elastic behaviors of textiles. Furthermore, we explore the effect
the dimension of the EC has on accurate force modeling of non-linear real-world
elastic behaviors, highlighting the challenges of current robotic setups to
sense textile properties
Learning to Grasp Clothing Structural Regions for Garment Manipulation Tasks
When performing cloth-related tasks, such as garment hanging, it is often
important to identify and grasp certain structural regions -- a shirt's collar
as opposed to its sleeve, for instance. However, due to cloth deformability,
these manipulation activities, which are essential in domestic, health care,
and industrial contexts, remain challenging for robots. In this paper, we focus
on how to segment and grasp structural regions of clothes to enable
manipulation tasks, using hanging tasks as case study. To this end, a neural
network-based perception system is proposed to segment a shirt's collar from
areas that represent the rest of the scene in a depth image. With a 10-minute
video of a human manipulating shirts to train it, our perception system is
capable of generalizing to other shirts regardless of texture as well as to
other types of collared garments. A novel grasping strategy is then proposed
based on the segmentation to determine grasping pose. Experiments demonstrate
that our proposed grasping strategy achieves 92\%, 80\%, and 50\% grasping
success rates with one folded garment, one crumpled garment and three crumpled
garments, respectively.
Our grasping strategy performs considerably better than tested baselines that
do not take into account the structural nature of the garments. With the
proposed region segmentation and grasping strategy, challenging garment hanging
tasks are successfully implemented using an open-loop control policy.
Supplementary material is available at
https://sites.google.com/view/garment-hangingComment: Accepted by IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS 2023
Learning to Singulate Layers of Cloth using Tactile Feedback
Robotic manipulation of cloth has applications ranging from fabrics
manufacturing to handling blankets and laundry. Cloth manipulation is
challenging for robots largely due to their high degrees of freedom, complex
dynamics, and severe self-occlusions when in folded or crumpled configurations.
Prior work on robotic manipulation of cloth relies primarily on vision sensors
alone, which may pose challenges for fine-grained manipulation tasks such as
grasping a desired number of cloth layers from a stack of cloth. In this paper,
we propose to use tactile sensing for cloth manipulation; we attach a tactile
sensor (ReSkin) to one of the two fingertips of a Franka robot and train a
classifier to determine whether the robot is grasping a specific number of
cloth layers. During test-time experiments, the robot uses this classifier as
part of its policy to grasp one or two cloth layers using tactile feedback to
determine suitable grasping points. Experimental results over 180 physical
trials suggest that the proposed method outperforms baselines that do not use
tactile feedback and has better generalization to unseen cloth compared to
methods that use image classifiers. Code, data, and videos are available at
https://sites.google.com/view/reskin-cloth.Comment: IROS 2022. See https://sites.google.com/view/reskin-cloth for
supplementary materia
Robotic cloth manipulation for clothing assistance task using Dynamic Movement Primitives
The need of robotic clothing assistance in the field of assistive robotics is growing, as it is one of the most basic and essential assistance activities in daily life of elderly and disabled people. In this study we are investigating the applicability of using Dynamic Movement Primitives (DMP) as a task parameterization model for performing clothing assistance task. Robotic cloth manipulation task deals with putting a clothing article on both the arms. Robot trajectory varies significantly for various postures and also there can be various failure scenarios while doing cooperative manipulation with non-rigid and highly deformable clothing article. We have performed experiments on soft mannequin instead of human. Result shows that DMPs are able to generalize movement trajectory for modified posture.3rd International Conference of Robotics Society of India (AIR \u2717: Advances in Robotics), June 28 - July 2, 2017, New Delhi, Indi
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