833 research outputs found
Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing
We present Neural Contact Fields, a method that brings together neural fields
and tactile sensing to address the problem of tracking extrinsic contact
between object and environment. Knowing where the external contact occurs is a
first step towards methods that can actively control it in facilitating
downstream manipulation tasks. Prior work for localizing environmental contacts
typically assume a contact type (e.g. point or line), does not capture
contact/no-contact transitions, and only works with basic geometric-shaped
objects. Neural Contact Fields are the first method that can track arbitrary
multi-modal extrinsic contacts without making any assumptions about the contact
type. Our key insight is to estimate the probability of contact for any 3D
point in the latent space of object shapes, given vision-based tactile inputs
that sense the local motion resulting from the external contact. In
experiments, we find that Neural Contact Fields are able to localize multiple
contact patches without making any assumptions about the geometry of the
contact, and capture contact/no-contact transitions for known categories of
objects with unseen shapes in unseen environment configurations. In addition to
Neural Contact Fields, we also release our YCB-Extrinsic-Contact dataset of
simulated extrinsic contact interactions to enable further research in this
area. Project page: https://github.com/carolinahiguera/NCFComment: 2023 International Conference on Robotics and Automation (ICRA
Perceiving Extrinsic Contacts from Touch Improves Learning Insertion Policies
Robotic manipulation tasks such as object insertion typically involve
interactions between object and environment, namely extrinsic contacts. Prior
work on Neural Contact Fields (NCF) use intrinsic tactile sensing between
gripper and object to estimate extrinsic contacts in simulation. However, its
effectiveness and utility in real-world tasks remains unknown.
In this work, we improve NCF to enable sim-to-real transfer and use it to
train policies for mug-in-cupholder and bowl-in-dishrack insertion tasks. We
find our model NCF-v2, is capable of estimating extrinsic contacts in the
real-world. Furthermore, our insertion policy with NCF-v2 outperforms policies
without it, achieving 33% higher success and 1.36x faster execution on
mug-in-cupholder, and 13% higher success and 1.27x faster execution on
bowl-in-dishrack.Comment: Under revie
Neuronal circuitry for pain processing in the dorsal horn
Neurons in the spinal dorsal horn process sensory information, which is then transmitted to several brain regions, including those responsible for pain perception. The dorsal horn provides numerous potential targets for the development of novel analgesics and is thought to undergo changes that contribute to the exaggerated pain felt after nerve injury and inflammation. Despite its obvious importance, we still know little about the neuronal circuits that process sensory information, mainly because of the heterogeneity of the various neuronal components that make up these circuits. Recent studies have begun to shed light on the neuronal organization and circuitry of this complex region
In-Hand Manipulation of Unknown Objects with Tactile Sensing for Insertion
In this paper, we present a method to manipulate unknown objects in-hand
using tactile sensing without relying on a known object model. In many cases,
vision-only approaches may not be feasible; for example, due to occlusion in
cluttered spaces. We address this limitation by introducing a method to
reorient unknown objects using tactile sensing. It incrementally builds a
probabilistic estimate of the object shape and pose during task-driven
manipulation. Our approach uses Bayesian optimization to balance exploration of
the global object shape with efficient task completion. To demonstrate the
effectiveness of our method, we apply it to a simulated Tactile-Enabled Roller
Grasper, a gripper that rolls objects in hand while collecting tactile data. We
evaluate our method on an insertion task with randomly generated objects and
find that it reliably reorients objects while significantly reducing the
exploration time
Inter-finger Small Object Manipulation with DenseTact Optical Tactile Sensor
The ability to grasp and manipulate small objects in cluttered environments
remains a significant challenge. This paper introduces a novel approach that
utilizes a tactile sensor-equipped gripper with eight degrees of freedom to
overcome these limitations. We employ DenseTact 2.0 for the gripper, enabling
precise control and improved grasp success rates, particularly for small
objects ranging from 5mm to 25mm. Our integrated strategy incorporates the
robot arm, gripper, and sensor to manipulate and orient small objects for
subsequent classification effectively. We contribute a specialized dataset
designed for classifying these objects based on tactile sensor output and a new
control algorithm for in-hand orientation tasks. Our system demonstrates 88% of
successful grasp and successfully classified small objects in cluttered
scenarios
Tactile-Filter: Interactive Tactile Perception for Part Mating
Humans rely on touch and tactile sensing for a lot of dexterous manipulation
tasks. Our tactile sensing provides us with a lot of information regarding
contact formations as well as geometric information about objects during any
interaction. With this motivation, vision-based tactile sensors are being
widely used for various robotic perception and control tasks. In this paper, we
present a method for interactive perception using vision-based tactile sensors
for a part mating task, where a robot can use tactile sensors and a feedback
mechanism using a particle filter to incrementally improve its estimate of
objects (pegs and holes) that fit together. To do this, we first train a deep
neural network that makes use of tactile images to predict the probabilistic
correspondence between arbitrarily shaped objects that fit together. The
trained model is used to design a particle filter which is used twofold. First,
given one partial (or non-unique) observation of the hole, it incrementally
improves the estimate of the correct peg by sampling more tactile observations.
Second, it selects the next action for the robot to sample the next touch (and
thus image) which results in maximum uncertainty reduction to minimize the
number of interactions during the perception task. We evaluate our method on
several part-mating tasks with novel objects using a robot equipped with a
vision-based tactile sensor. We also show the efficiency of the proposed action
selection method against a naive method. See supplementary video at
https://www.youtube.com/watch?v=jMVBg_e3gLw .Comment: Accepted at RSS202
Enabling Robot Manipulation of Soft and Rigid Objects with Vision-based Tactile Sensors
Endowing robots with tactile capabilities opens up new possibilities for
their interaction with the environment, including the ability to handle fragile
and/or soft objects. In this work, we equip the robot gripper with low-cost
vision-based tactile sensors and propose a manipulation algorithm that adapts
to both rigid and soft objects without requiring any knowledge of their
properties. The algorithm relies on a touch and slip detection method, which
considers the variation in the tactile images with respect to reference ones.
We validate the approach on seven different objects, with different properties
in terms of rigidity and fragility, to perform unplugging and lifting tasks.
Furthermore, to enhance applicability, we combine the manipulation algorithm
with a grasp sampler for the task of finding and picking a grape from a bunch
without damaging~it.Comment: Published in IEEE International Conference on Automation Science and
Engineering (CASE2023
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