557 research outputs found
Learning to Navigate Cloth using Haptics
We present a controller that allows an arm-like manipulator to navigate
deformable cloth garments in simulation through the use of haptic information.
The main challenge of such a controller is to avoid getting tangled in, tearing
or punching through the deforming cloth. Our controller aggregates force
information from a number of haptic-sensing spheres all along the manipulator
for guidance. Based on haptic forces, each individual sphere updates its target
location, and the conflicts that arise between this set of desired positions is
resolved by solving an inverse kinematic problem with constraints.
Reinforcement learning is used to train the controller for a single
haptic-sensing sphere, where a training run is terminated (and thus penalized)
when large forces are detected due to contact between the sphere and a
simplified model of the cloth. In simulation, we demonstrate successful
navigation of a robotic arm through a variety of garments, including an
isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two
baseline controllers: one without haptics and another that was trained based on
large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A.
Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm
Deep Learning of Force Manifolds from the Simulated Physics of Robotic Paper Folding
Robotic manipulation of slender objects is challenging, especially when the
induced deformations are large and nonlinear. Traditionally, learning-based
control approaches, such as imitation learning, have been used to address
deformable material manipulation. These approaches lack generality and often
suffer critical failure from a simple switch of material, geometric, and/or
environmental (e.g., friction) properties. This article tackles a fundamental
but difficult deformable manipulation task: forming a predefined fold in paper
with only a single manipulator. A data-driven framework combining
physically-accurate simulation and machine learning is used to train a deep
neural network capable of predicting the external forces induced on the
manipulated paper given a grasp position. We frame the problem using scaling
analysis, resulting in a control framework robust against material and
geometric changes. Path planning is then carried out over the generated "neural
force manifold" to produce robot manipulation trajectories optimized to prevent
sliding, with offline trajectory generation finishing 15 faster than
previous physics-based folding methods. The inference speed of the trained
model enables the incorporation of real-time visual feedback to achieve
closed-loop sensorimotor control. Real-world experiments demonstrate that our
framework can greatly improve robotic manipulation performance compared to
state-of-the-art folding strategies, even when manipulating paper objects of
various materials and shapes.Comment: Supplementary video is available on YouTube:
https://youtu.be/k0nexYGy-P
Feedback-based Fabric Strip Folding
Accurate manipulation of a deformable body such as a piece of fabric is
difficult because of its many degrees of freedom and unobservable properties
affecting its dynamics. To alleviate these challenges, we propose the
application of feedback-based control to robotic fabric strip folding. The
feedback is computed from the low dimensional state extracted from a camera
image. We trained the controller using reinforcement learning in simulation
which was calibrated to cover the real fabric strip behaviors. The proposed
feedback-based folding was experimentally compared to two state-of-the-art
folding methods and our method outperformed both of them in terms of accuracy.Comment: Submitted to IEEE/RSJ IROS201
DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics
Deformable Object Manipulation (DOM) is of significant importance to both
daily and industrial applications. Recent successes in differentiable physics
simulators allow learning algorithms to train a policy with analytic gradients
through environment dynamics, which significantly facilitates the development
of DOM algorithms. However, existing DOM benchmarks are either
single-object-based or non-differentiable. This leaves the questions of 1) how
a task-specific algorithm performs on other tasks and 2) how a
differentiable-physics-based algorithm compares with the non-differentiable
ones in general. In this work, we present DaXBench, a differentiable DOM
benchmark with a wide object and task coverage. DaXBench includes 9 challenging
high-fidelity simulated tasks, covering rope, cloth, and liquid manipulation
with various difficulty levels. To better understand the performance of general
algorithms on different DOM tasks, we conduct comprehensive experiments over
representative DOM methods, ranging from planning to imitation learning and
reinforcement learning. In addition, we provide careful empirical studies of
existing decision-making algorithms based on differentiable physics, and
discuss their limitations, as well as potential future directions.Comment: ICLR 2023 (Oral
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
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