64,817 research outputs found
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
GoNet: An Approach-Constrained Generative Grasp Sampling Network
Constraining the approach direction of grasps is important when picking
objects in confined spaces, such as when emptying a shelf. Yet, such
capabilities are not available in state-of-the-art data-driven grasp sampling
methods that sample grasps all around the object. In this work, we address the
specific problem of training approach-constrained data-driven grasp samplers
and how to generate good grasping directions automatically. Our solution is
GoNet: a generative grasp sampler that can constrain the grasp approach
direction to lie close to a specified direction. This is achieved by
discretizing SO(3) into bins and training GoNet to generate grasps from those
bins. At run-time, the bin aligning with the second largest principal component
of the observed point cloud is selected. GoNet is benchmarked against GraspNet,
a state-of-the-art unconstrained grasp sampler, in an unconfined grasping
experiment in simulation and on an unconfined and confined grasping experiment
in the real world. The results demonstrate that GoNet achieves higher
success-over-coverage in simulation and a 12%-18% higher success rate in
real-world table-picking and shelf-picking tasks than the baseline.Comment: IROS 2023 submissio
Experiments with hierarchical reinforcement learning of multiple grasping policies
Robotic grasping has attracted considerable interest, but it
still remains a challenging task. The data-driven approach is a promising
solution to the robotic grasping problem; this approach leverages a
grasp dataset and generalizes grasps for various objects. However, these
methods often depend on the quality of the given datasets, which are not
trivial to obtain with sufficient quality. Although reinforcement learning
approaches have been recently used to achieve autonomous collection
of grasp datasets, the existing algorithms are often limited to specific
grasp types. In this paper, we present a framework for hierarchical reinforcement
learning of grasping policies. In our framework, the lowerlevel
hierarchy learns multiple grasp types, and the upper-level hierarchy
learns a policy to select from the learned grasp types according to a point
cloud of a new object. Through experiments, we validate that our approach
learns grasping by constructing the grasp dataset autonomously.
The experimental results show that our approach learns multiple grasping
policies and generalizes the learned grasps by using local point cloud
information
Safe Grasping with a Force Controlled Soft Robotic Hand
Safe yet stable grasping requires a robotic hand to apply sufficient force on
the object to immobilize it while keeping it from getting damaged. Soft robotic
hands have been proposed for safe grasping due to their passive compliance, but
even such a hand can crush objects if the applied force is too high. Thus for
safe grasping, regulating the grasping force is of uttermost importance even
with soft hands. In this work, we present a force controlled soft hand and use
it to achieve safe grasping. To this end, resistive force and bend sensors are
integrated in a soft hand, and a data-driven calibration method is proposed to
estimate contact interaction forces. Given the force readings, the pneumatic
pressures are regulated using a proportional-integral controller to achieve
desired force. The controller is experimentally evaluated and benchmarked by
grasping easily deformable objects such as plastic and paper cups without
neither dropping nor deforming them. Together, the results demonstrate that our
force controlled soft hand can grasp deformable objects in a safe yet stable
manner.Comment: Accepted to 2020 IEEE International Conference on Systems, Man, and
Cybernetics (IEEE SMC 2020
Ab Initio Particle-based Object Manipulation
This paper presents Particle-based Object Manipulation (Prompt), a new
approach to robot manipulation of novel objects ab initio, without prior object
models or pre-training on a large object data set. The key element of Prompt is
a particle-based object representation, in which each particle represents a
point in the object, the local geometric, physical, and other features of the
point, and also its relation with other particles. Like the model-based
analytic approaches to manipulation, the particle representation enables the
robot to reason about the object's geometry and dynamics in order to choose
suitable manipulation actions. Like the data-driven approaches, the particle
representation is learned online in real-time from visual sensor input,
specifically, multi-view RGB images. The particle representation thus connects
visual perception with robot control. Prompt combines the benefits of both
model-based reasoning and data-driven learning. We show empirically that Prompt
successfully handles a variety of everyday objects, some of which are
transparent. It handles various manipulation tasks, including grasping,
pushing, etc,. Our experiments also show that Prompt outperforms a
state-of-the-art data-driven grasping method on the daily objects, even though
it does not use any offline training data.Comment: Robotics: Science and Systems (RSS) 202
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