607 research outputs found
Force-Guided High-Precision Grasping Control of Fragile and Deformable Objects Using sEMG-Based Force Prediction
Regulating contact forces with high precision is crucial for grasping and
manipulating fragile or deformable objects. We aim to utilize the dexterity of
human hands to regulate the contact forces for robotic hands and exploit human
sensory-motor synergies in a wearable and non-invasive way. We extracted force
information from the electric activities of skeletal muscles during their
voluntary contractions through surface electromyography (sEMG). We built a
regression model based on a Neural Network to predict the gripping force from
the preprocessed sEMG signals and achieved high accuracy (R2 = 0.982). Based on
the force command predicted from human muscles, we developed a force-guided
control framework, where force control was realized via an admittance
controller that tracked the predicted gripping force reference to grasp
delicate and deformable objects. We demonstrated the effectiveness of the
proposed method on a set of representative fragile and deformable objects from
daily life, all of which were successfully grasped without any damage or
deformation.Comment: 8 pages, 11 figures, to be published on IEEE Robotics and Automation
Letters. For the attached video, see https://youtu.be/0AotKaWFJD
Learning Latent Space Dynamics for Tactile Servoing
To achieve a dexterous robotic manipulation, we need to endow our robot with
tactile feedback capability, i.e. the ability to drive action based on tactile
sensing. In this paper, we specifically address the challenge of tactile
servoing, i.e. given the current tactile sensing and a target/goal tactile
sensing --memorized from a successful task execution in the past-- what is the
action that will bring the current tactile sensing to move closer towards the
target tactile sensing at the next time step. We develop a data-driven approach
to acquire a dynamics model for tactile servoing by learning from
demonstration. Moreover, our method represents the tactile sensing information
as to lie on a surface --or a 2D manifold-- and perform a manifold learning,
making it applicable to any tactile skin geometry. We evaluate our method on a
contact point tracking task using a robot equipped with a tactile finger. A
video demonstrating our approach can be seen in https://youtu.be/0QK0-Vx7WkIComment: Accepted to be published at the International Conference on Robotics
and Automation (ICRA) 2019. The final version for publication at ICRA 2019 is
7 pages (i.e. 6 pages of technical content (including text, figures, tables,
acknowledgement, etc.) and 1 page of the Bibliography/References), while this
arXiv version is 8 pages (added Appendix and some extra details
VIRDO++: Real-World, Visuo-tactile Dynamics and Perception of Deformable Objects
Deformable objects manipulation can benefit from representations that
seamlessly integrate vision and touch while handling occlusions. In this work,
we present a novel approach for, and real-world demonstration of, multimodal
visuo-tactile state-estimation and dynamics prediction for deformable objects.
Our approach, VIRDO++, builds on recent progress in multimodal neural implicit
representations for deformable object state-estimation [1] via a new
formulation for deformation dynamics and a complementary state-estimation
algorithm that (i) maintains a belief over deformations, and (ii) enables
practical real-world application by removing the need for privileged contact
information. In the context of two real-world robotic tasks, we show:(i)
high-fidelity cross-modal state-estimation and prediction of deformable objects
from partial visuo-tactile feedback, and (ii) generalization to unseen objects
and contact formations
Deep-LfD: Deep robot learning from demonstrations
Like other robot learning from demonstration (LfD) approaches, deep-LfD builds a task model from sample demonstrations. However, unlike conventional LfD, the deep-LfD model learns the relation between high dimensional visual sensory information and robot trajectory/path. This paper presents a dataset of successful needle insertion by da Vinci Research Kit into deformable objects based on which several deep-LfD models are built as a benchmark of models learning robot controller for the needle insertion task
Robot Learning for Manipulation of Deformable Linear Objects
Deformable Object Manipulation (DOM) is a challenging problem in robotics. Until recently there has been limited research on the subject, with most robotic manipulation methods being developed for rigid objects. Part of the challenge in DOM is that non-rigid objects require solutions capable of generalizing to changes in shape and mechanical properties. Recently, Machine Learning (ML) has been proven successful in other fields where generalization is important such as computer vision, thus encouraging the application of ML to robotics as well. Notably, Reinforcement Learning (RL) has shown promise in finding control policies for manipulation of rigid objects. However, RL requires large amounts of data that are better satisfied in simulation while deformable objects are inherently more difficult to model and simulate. This thesis presents ReForm, a simulation sandbox for robotic manipulation of Deformable Linear Objects (DLOs) such as cables, ropes, and wires. DLO manipulation is an interesting problem for a variety of applications throughout manufacturing, agriculture, and medicine. Currently, this sandbox includes six shape control tasks, which are classified as explicit when a precise shape is to be achieved, or implicit when the deformation is just a consequence of a more abstract goal, e.g. wrapping a DLO around another object. The proposed simulation environments aim to facilitate comparison and reproducibility of robot learning research. To that end, an RL algorithm is tested on each simulated task providing initial benchmarking results. ReForm is one of three concurrent frameworks to first support DOM problems. This thesis also addresses the problem of DLO state representation for an explicit shape control problem. Moreover, the effects of elastoplastic properties on the RL reward definition are investigated. From a control perspective, DLOs with these properties are particularly challenging to manipulate due to their nonlinear behavior, acting elastic up to a yield point after which they become permanently deformed. A low-dimensional representation from discrete differential geometry is proposed, offering more descriptive shape information than a simple point-cloud while avoiding the need for curve fitting. Empirical results show that this representation leads to a better goal description in the presence of elastoplasticity, preventing the RL algorithm from converging to local minima which correspond to incorrect shapes of the DLO
Learning to Efficiently Plan Robust Frictional Multi-Object Grasps
We consider a decluttering problem where multiple rigid convex polygonal
objects rest in randomly placed positions and orientations on a planar surface
and must be efficiently transported to a packing box using both single and
multi-object grasps. Prior work considered frictionless multi-object grasping.
In this paper, we introduce friction to increase picks per hour. We train a
neural network using real examples to plan robust multi-object grasps. In
physical experiments, we find a 13.7% increase in success rate, a 1.6x increase
in picks per hour, and a 6.3x decrease in grasp planning time compared to prior
work on multi-object grasping. Compared to single object grasping, we find a
3.1x increase in picks per hour
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