2 research outputs found
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
AllSight: A Low-Cost and High-Resolution Round Tactile Sensor with Zero-Shot Learning Capability
Tactile sensing is a necessary capability for a robotic hand to perform fine
manipulations and interact with the environment. Optical sensors are a
promising solution for high-resolution contact estimation. Nevertheless, they
are usually not easy to fabricate and require individual calibration in order
to acquire sufficient accuracy. In this letter, we propose AllSight, an optical
tactile sensor with a round 3D structure potentially designed for robotic
in-hand manipulation tasks. AllSight is mostly 3D printed making it low-cost,
modular, durable and in the size of a human thumb while with a large contact
surface. We show the ability of AllSight to learn and estimate a full contact
state, i.e., contact position, forces and torsion. With that, an experimental
benchmark between various configurations of illumination and contact elastomers
are provided. Furthermore, the robust design of AllSight provides it with a
unique zero-shot capability such that a practitioner can fabricate the
open-source design and have a ready-to-use state estimation model. A set of
experiments demonstrates the accurate state estimation performance of AllSight