8,941 research outputs found
Optical Proximity Sensing for Pose Estimation During In-Hand Manipulation
During in-hand manipulation, robots must be able to continuously estimate the
pose of the object in order to generate appropriate control actions. The
performance of algorithms for pose estimation hinges on the robot's sensors
being able to detect discriminative geometric object features, but previous
sensing modalities are unable to make such measurements robustly. The robot's
fingers can occlude the view of environment- or robot-mounted image sensors,
and tactile sensors can only measure at the local areas of contact. Motivated
by fingertip-embedded proximity sensors' robustness to occlusion and ability to
measure beyond the local areas of contact, we present the first evaluation of
proximity sensor based pose estimation for in-hand manipulation. We develop a
novel two-fingered hand with fingertip-embedded optical time-of-flight
proximity sensors as a testbed for pose estimation during planar in-hand
manipulation. Here, the in-hand manipulation task consists of the robot moving
a cylindrical object from one end of its workspace to the other. We
demonstrate, with statistical significance, that proximity-sensor based pose
estimation via particle filtering during in-hand manipulation: a) exhibits 50%
lower average pose error than a tactile-sensor based baseline; b) empowers a
model predictive controller to achieve 30% lower final positioning error
compared to when using tactile-sensor based pose estimates.Comment: 8 pages, 6 figure
FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback
In this paper, we address the problem of using visuo-tactile feedback for
6-DoF localization and 3D reconstruction of unknown in-hand objects. We propose
FingerSLAM, a closed-loop factor graph-based pose estimator that combines local
tactile sensing at finger-tip and global vision sensing from a wrist-mount
camera. FingerSLAM is constructed with two constituent pose estimators: a
multi-pass refined tactile-based pose estimator that captures movements from
detailed local textures, and a single-pass vision-based pose estimator that
predicts from a global view of the object. We also design a loop closure
mechanism that actively matches current vision and tactile images to previously
stored key-frames to reduce accumulated error. FingerSLAM incorporates the two
sensing modalities of tactile and vision, as well as the loop closure mechanism
with a factor graph-based optimization framework. Such a framework produces an
optimized pose estimation solution that is more accurate than the standalone
estimators. The estimated poses are then used to reconstruct the shape of the
unknown object incrementally by stitching the local point clouds recovered from
tactile images. We train our system on real-world data collected with 20
objects. We demonstrate reliable visuo-tactile pose estimation and shape
reconstruction through quantitative and qualitative real-world evaluations on 6
objects that are unseen during training.Comment: Submitted and accepted to 2023 IEEE International Conference on
Robotics and Automation (ICRA 2023
Learning Haptic-based Object Pose Estimation for In-hand Manipulation Control with Underactuated Robotic Hands
Unlike traditional robotic hands, underactuated compliant hands are
challenging to model due to inherent uncertainties. Consequently, pose
estimation of a grasped object is usually performed based on visual perception.
However, visual perception of the hand and object can be limited in occluded or
partly-occluded environments. In this paper, we aim to explore the use of
haptics, i.e., kinesthetic and tactile sensing, for pose estimation and in-hand
manipulation with underactuated hands. Such haptic approach would mitigate
occluded environments where line-of-sight is not always available. We put an
emphasis on identifying the feature state representation of the system that
does not include vision and can be obtained with simple and low-cost hardware.
For tactile sensing, therefore, we propose a low-cost and flexible sensor that
is mostly 3D printed along with the finger-tip and can provide implicit contact
information. Taking a two-finger underactuated hand as a test-case, we analyze
the contribution of kinesthetic and tactile features along with various
regression models to the accuracy of the predictions. Furthermore, we propose a
Model Predictive Control (MPC) approach which utilizes the pose estimation to
manipulate objects to desired states solely based on haptics. We have conducted
a series of experiments that validate the ability to estimate poses of various
objects with different geometry, stiffness and texture, and show manipulation
to goals in the workspace with relatively high accuracy
Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects
Robotic manipulation, in particular in-hand object manipulation, often
requires an accurate estimate of the object's 6D pose. To improve the accuracy
of the estimated pose, state-of-the-art approaches in 6D object pose estimation
use observational data from one or more modalities, e.g., RGB images, depth,
and tactile readings. However, existing approaches make limited use of the
underlying geometric structure of the object captured by these modalities,
thereby, increasing their reliance on visual features. This results in poor
performance when presented with objects that lack such visual features or when
visual features are simply occluded. Furthermore, current approaches do not
take advantage of the proprioceptive information embedded in the position of
the fingers. To address these limitations, in this paper: (1) we introduce a
hierarchical graph neural network architecture for combining multimodal (vision
and touch) data that allows for a geometrically informed 6D object pose
estimation, (2) we introduce a hierarchical message passing operation that
flows the information within and across modalities to learn a graph-based
object representation, and (3) we introduce a method that accounts for the
proprioceptive information for in-hand object representation. We evaluate our
model on a diverse subset of objects from the YCB Object and Model Set, and
show that our method substantially outperforms existing state-of-the-art work
in accuracy and robustness to occlusion. We also deploy our proposed framework
on a real robot and qualitatively demonstrate successful transfer to real
settings
Tactile Mapping and Localization from High-Resolution Tactile Imprints
This work studies the problem of shape reconstruction and object localization
using a vision-based tactile sensor, GelSlim. The main contributions are the
recovery of local shapes from contact, an approach to reconstruct the tactile
shape of objects from tactile imprints, and an accurate method for object
localization of previously reconstructed objects. The algorithms can be applied
to a large variety of 3D objects and provide accurate tactile feedback for
in-hand manipulation. Results show that by exploiting the dense tactile
information we can reconstruct the shape of objects with high accuracy and do
on-line object identification and localization, opening the door to reactive
manipulation guided by tactile sensing. We provide videos and supplemental
information in the project's website
http://web.mit.edu/mcube/research/tactile_localization.html.Comment: ICRA 2019, 7 pages, 7 figures. Website:
http://web.mit.edu/mcube/research/tactile_localization.html Video:
https://youtu.be/uMkspjmDbq
Realtime State Estimation with Tactile and Visual sensing. Application to Planar Manipulation
Accurate and robust object state estimation enables successful object
manipulation. Visual sensing is widely used to estimate object poses. However,
in a cluttered scene or in a tight workspace, the robot's end-effector often
occludes the object from the visual sensor. The robot then loses visual
feedback and must fall back on open-loop execution.
In this paper, we integrate both tactile and visual input using a framework
for solving the SLAM problem, incremental smoothing and mapping (iSAM), to
provide a fast and flexible solution. Visual sensing provides global pose
information but is noisy in general, whereas contact sensing is local, but its
measurements are more accurate relative to the end-effector. By combining them,
we aim to exploit their advantages and overcome their limitations. We explore
the technique in the context of a pusher-slider system. We adapt iSAM's
measurement cost and motion cost to the pushing scenario, and use an
instrumented setup to evaluate the estimation quality with different object
shapes, on different surface materials, and under different contact modes
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