23 research outputs found
Towards Assistive Feeding with a General-Purpose Mobile Manipulator
General-purpose mobile manipulators have the potential to serve as a
versatile form of assistive technology. However, their complexity creates
challenges, including the risk of being too difficult to use. We present a
proof-of-concept robotic system for assistive feeding that consists of a Willow
Garage PR2, a high-level web-based interface, and specialized autonomous
behaviors for scooping and feeding yogurt. As a step towards use by people with
disabilities, we evaluated our system with 5 able-bodied participants. All 5
successfully ate yogurt using the system and reported high rates of success for
the system's autonomous behaviors. Also, Henry Evans, a person with severe
quadriplegia, operated the system remotely to feed an able-bodied person. In
general, people who operated the system reported that it was easy to use,
including Henry. The feeding system also incorporates corrective actions
designed to be triggered either autonomously or by the user. In an offline
evaluation using data collected with the feeding system, a new version of our
multimodal anomaly detection system outperformed prior versions.Comment: This short 4-page paper was accepted and presented as a poster on
May. 16, 2016 in ICRA 2016 workshop on 'Human-Robot Interfaces for Enhanced
Physical Interactions' organized by Arash Ajoudani, Barkan Ugurlu, Panagiotis
Artemiadis, Jun Morimoto. It was peer reviewed by one reviewe
Graph-based 3D Collision-distance Estimation Network with Probabilistic Graph Rewiring
We aim to solve the problem of data-driven collision-distance estimation
given 3-dimensional (3D) geometries. Conventional algorithms suffer from low
accuracy due to their reliance on limited representations, such as point
clouds. In contrast, our previous graph-based model, GraphDistNet, achieves
high accuracy using edge information but incurs higher message-passing costs
with growing graph size, limiting its applicability to 3D geometries. To
overcome these challenges, we propose GDN-R, a novel 3D graph-based estimation
network.GDN-R employs a layer-wise probabilistic graph-rewiring algorithm
leveraging the differentiable Gumbel-top-K relaxation. Our method accurately
infers minimum distances through iterative graph rewiring and updating relevant
embeddings. The probabilistic rewiring enables fast and robust embedding with
respect to unforeseen categories of geometries. Through 41,412 random benchmark
tasks with 150 pairs of 3D objects, we show GDN-R outperforms state-of-the-art
baseline methods in terms of accuracy and generalizability. We also show that
the proposed rewiring improves the update performance reducing the size of the
estimation model. We finally show its batch prediction and auto-differentiation
capabilities for trajectory optimization in both simulated and real-world
scenarios.Comment: 7 pages, 6 figure
SGGNet: Speech-Scene Graph Grounding Network for Speech-guided Navigation
The spoken language serves as an accessible and efficient interface, enabling
non-experts and disabled users to interact with complex assistant robots.
However, accurately grounding language utterances gives a significant challenge
due to the acoustic variability in speakers' voices and environmental noise. In
this work, we propose a novel speech-scene graph grounding network (SGGNet)
that robustly grounds spoken utterances by leveraging the acoustic similarity
between correctly recognized and misrecognized words obtained from automatic
speech recognition (ASR) systems. To incorporate the acoustic similarity, we
extend our previous grounding model, the scene-graph-based grounding network
(SGGNet), with the ASR model from NVIDIA NeMo. We accomplish this by feeding
the latent vector of speech pronunciations into the BERT-based grounding
network within SGGNet. We evaluate the effectiveness of using latent vectors of
speech commands in grounding through qualitative and quantitative studies. We
also demonstrate the capability of SGGNet in a speech-based navigation task
using a real quadruped robot, RBQ-3, from Rainbow Robotics.Comment: 7 pages, 6 figures, Paper accepted for the Special Session at the
2023 International Symposium on Robot and Human Interactive Communication
(RO-MAN), [Dohyun Kim, Yeseung Kim, Jaehwi Jang, and Minjae Song] contributed
equally to this wor
Aptamer-based single-molecule imaging of insulin receptors in living cells
We present a single-molecule imaging platform that quantitatively explores the spatiotemporal dynamics of individual insulin receptors in living cells. Modified DNA aptamers that specifically recognize insulin receptors (IRs) with a high affinity were selected through the SELEX process. Using quantum dot-labeled aptamers, we successfully imaged and analyzed the diffusive motions of individual IRs in the plasma membranes of a variety of cell lines (HIR, HEK293, HepG2). We further explored the cholesterol-dependent movement of IRs to address whether cholesterol depletion interferes with IRs and found that cholesterol depletion of the plasma membrane by methyl-??-cyclodextrin reduces the mobility of IRs. The aptamer-based single-molecule imaging of IRs will provide better understanding of insulin signal transduction through the dynamics study of IRs in the plasma membrane.open1
GraphDistNet: A Graph-based Collision-distance Estimator for Gradient-based Trajectory
Trajectory optimization (TO) aims to find a sequence of valid states while
minimizing costs. However, its fine validation process is often costly due to
computationally expensive collision searches, otherwise coarse searches lower
the safety of the system losing a precise solution. To resolve the issues, we
introduce a new collision-distance estimator, GraphDistNet, that can precisely
encode the structural information between two geometries by leveraging edge
feature-based convolutional operations, and also efficiently predict a batch of
collision distances and gradients through 25,000 random environments with a
maximum of 20 unforeseen objects. Further, we show the adoption of attention
mechanism enables our method to be easily generalized in unforeseen complex
geometries toward TO. Our evaluation show GraphDistNet outperforms
state-of-the-art baseline methods in both simulated and real world tasks.Comment: 8 pages, 7 figures, submitted to RA-L with IROS 2022 Optio
Coordinating Multi-Protein Mismatch Repair by Managing Diffusion Mechanics on the DNA
DNA mismatch repair (MMR) corrects DNA base-pairing errors that occur during DNA replication. MMR catalyzes strand-specific DNA degradation and resynthesis by dynamic molecular coordination of sequential downstream pathways. The temporal and mechanistic order of molecular events is essential to insure interactions in MMR that occur over long distances on the DNA. Biophysical real-time studies of highly conserved components on mismatched DNA have shed light on the mechanics of MMR. Single-molecule imaging has visualized stochastically coordinated MMR interactions that are based on thermal fluctuation driven motions. In this review, we describe the role of diffusivity and stochasticity in MMR beginning with mismatch recognition through strand-specific excision. We conclude with a perspective of the possible research directions that should solve the remaining questions in MMR. (C) 2018 Elsevier Ltd. All rights reserved.11Nsciescopu