174 research outputs found
Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs
The human reasoning process is seldom a one-way process from an input leading
to an output. Instead, it often involves a systematic deduction by ruling out
other possible outcomes as a self-checking mechanism. In this paper, we
describe the design of a hybrid neural network for logical learning that is
similar to the human reasoning through the introduction of an auxiliary input,
namely the indicators, that act as the hints to suggest logical outcomes. We
generate these indicators by digging into the hidden information buried
underneath the original training data for direct or indirect suggestions. We
used the MNIST data to demonstrate the design and use of these indicators in a
convolutional neural network. We trained a series of such hybrid neural
networks with variations of the indicators. Our results show that these hybrid
neural networks are very robust in generating logical outcomes with inherently
higher prediction accuracy than the direct use of the original input and output
in apparent models. Such improved predictability with reassured logical
confidence is obtained through the exhaustion of all possible indicators to
rule out all illogical outcomes, which is not available in the apparent models.
Our logical learning process can effectively cope with the unknown unknowns
using a full exploitation of all existing knowledge available for learning. The
design and implementation of the hints, namely the indicators, become an
essential part of artificial intelligence for logical learning. We also
introduce an ongoing application setup for this hybrid neural network in an
autonomous grasping robot, namely as_DeepClaw, aiming at learning an optimized
grasping pose through logical learning.Comment: 11 pages, 9 figures, 4 table
Proprioceptive Learning with Soft Polyhedral Networks
Proprioception is the "sixth sense" that detects limb postures with motor
neurons. It requires a natural integration between the musculoskeletal systems
and sensory receptors, which is challenging among modern robots that aim for
lightweight, adaptive, and sensitive designs at a low cost. Here, we present
the Soft Polyhedral Network with an embedded vision for physical interactions,
capable of adaptive kinesthesia and viscoelastic proprioception by learning
kinetic features. This design enables passive adaptations to omni-directional
interactions, visually captured by a miniature high-speed motion tracking
system embedded inside for proprioceptive learning. The results show that the
soft network can infer real-time 6D forces and torques with accuracies of
0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also
incorporate viscoelasticity in proprioception during static adaptation by
adding a creep and relaxation modifier to refine the predicted results. The
proposed soft network combines simplicity in design, omni-adaptation, and
proprioceptive sensing with high accuracy, making it a versatile solution for
robotics at a low cost with more than 1 million use cycles for tasks such as
sensitive and competitive grasping, and touch-based geometry reconstruction.
This study offers new insights into vision-based proprioception for soft robots
in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International
Journal of Robotics Research for revie
Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger
Robotic fingers made of soft material and compliant structures usually lead
to superior adaptation when interacting with the unstructured physical
environment. In this paper, we present an embedded sensing solution using
optical fibers for an omni-adaptive soft robotic finger with exceptional
adaptation in all directions. In particular, we managed to insert a pair of
optical fibers inside the finger's structural cavity without interfering with
its adaptive performance. The resultant integration is scalable as a versatile,
low-cost, and moisture-proof solution for physically safe human-robot
interaction. In addition, we experimented with our finger design for an object
sorting task and identified sectional diameters of 94\% objects within the
6mm error and measured 80\% of the structural strains within 0.1mm/mm
error. The proposed sensor design opens many doors in future applications of
soft robotics for scalable and adaptive physical interactions in the
unstructured environment.Comment: 8 pages, 6 figures, full-length version of a submission to IEEE
RoboSoft 202
Underwater Intention Recognition using Head Motion and Throat Vibration for Supernumerary Robotic Assistance
This study presents a multi-modal mechanism for recognizing human intentions
while diving underwater, aiming to achieve natural human-robot interactions
through an underwater superlimb for diving assistance. The underwater
environment severely limits the divers' capabilities in intention expression,
which becomes more challenging when they intend to operate tools while keeping
control of body postures in 3D with the various diving suits and gears. The
current literature is limited in underwater intention recognition, impeding the
development of intelligent wearable systems for human-robot interactions
underwater. Here, we present a novel solution to simultaneously detect head
motion and throat vibrations under the water in a compact, wearable design.
Experiment results show that using machine learning algorithms, we achieved
high performance in integrating these two modalities to translate human
intentions to robot control commands for an underwater superlimb system. This
study's results paved the way for future development in underwater intention
recognition and underwater human-robot interactions with supernumerary support.Comment: 6 pages, 9 figures, 3 tables, accepted to IEEE CASE 202
The Design of Crowd-Funded Products
Crowdfunding is an emerging phenomenon where entrepreneurs publicize their product concepts to raise development funding and collect design feedback directly from potential supporters. Many innovative products have raised a significant amount of crowdfunding. This paper analyzes the crowd-funded products to develop design guidelines for crowdfunding success. A database of 127 samples is collected in two different product categories from two different crowdfunding websites. They are evaluated using a design project assessment scorecard, the Real-Win-Worth framework, which focuses on the state of maturity on various customer, technical and supply chain dimensions. Our analysis identified key RWW factors that characterize successful design for crowd-funded products. For example, success at crowdfunding is attained through clear explanation of how the design operates technically and meets customer needs. Another recommendation is to not emphasize patent protection, for which crowd-funders are less concerned. Also, evidence of a strong startup financial plan is not necessary for crowdfunding success. These key RWW factors provide guidelines for designers and engineers to improve their design and validate their concepts early to improve their chances for success on crowdfunding platforms.SUTD-MIT International Design Centre (IDC
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