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

SGGNet2^2: 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$^2$) 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$^2$ 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

DNA hanger:novel surface-free/multiplexed single-molecule blotting platform

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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

Envelope Tracking PA and Doherty PA for Handset application

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Protein foci in live cells

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