821 research outputs found
Optimization Model for Planning Precision Grasps with Multi-Fingered Hands
Precision grasps with multi-fingered hands are important for precise
placement and in-hand manipulation tasks. Searching precision grasps on the
object represented by point cloud, is challenging due to the complex object
shape, high-dimensionality, collision and undesired properties of the sensing
and positioning. This paper proposes an optimization model to search for
precision grasps with multi-fingered hands. The model takes noisy point cloud
of the object as input and optimizes the grasp quality by iteratively searching
for the palm pose and finger joints positions. The collision between the hand
and the object is approximated and penalized by a series of least-squares. The
collision approximation is able to handle the point cloud representation of the
objects with complex shapes. The proposed optimization model is able to locate
collision-free optimal precision grasps efficiently. The average computation
time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise
of the point cloud. The effectiveness of the algorithm is demonstrated by
experiments.Comment: Submitted to IROS2019, experiment on BarrettHand, 8 page
CASSL: Curriculum Accelerated Self-Supervised Learning
Recent self-supervised learning approaches focus on using a few thousand data
points to learn policies for high-level, low-dimensional action spaces.
However, scaling this framework for high-dimensional control require either
scaling up the data collection efforts or using a clever sampling strategy for
training. We present a novel approach - Curriculum Accelerated Self-Supervised
Learning (CASSL) - to train policies that map visual information to high-level,
higher- dimensional action spaces. CASSL orders the sampling of training data
based on control dimensions: the learning and sampling are focused on few
control parameters before other parameters. The right curriculum for learning
is suggested by variance-based global sensitivity analysis of the control
space. We apply our CASSL framework to learning how to grasp using an adaptive,
underactuated multi-fingered gripper, a challenging system to control. Our
experimental results indicate that CASSL provides significant improvement and
generalization compared to baseline methods such as staged curriculum learning
(8% increase) and complete end-to-end learning with random exploration (14%
improvement) tested on a set of novel objects
Adaptive Motion Planning for Multi-fingered Functional Grasp via Force Feedback
Enabling multi-fingered robots to grasp and manipulate objects with
human-like dexterity is especially challenging during the dynamic, continuous
hand-object interactions. Closed-loop feedback control is essential for
dexterous hands to dynamically finetune hand poses when performing precise
functional grasps. This work proposes an adaptive motion planning method based
on deep reinforcement learning to adjust grasping poses according to real-time
feedback from joint torques from pre-grasp to goal grasp. We find the
multi-joint torques of the dexterous hand can sense object positions through
contacts and collisions, enabling real-time adjustment of grasps to generate
varying grasping trajectories for objects in different positions. In our
experiments, the performance gap with and without force feedback reveals the
important role of force feedback in adaptive manipulation. Our approach
utilizing force feedback preliminarily exhibits human-like flexibility,
adaptability, and precision.Comment: 8 pages,7 figure
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
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