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

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

Measurement of Hydrogen Direct Injection Jet Equivalence Ratio under Elevated Ambient Pressure Condition

Owing to climate change issues caused by global warming, the role of alternative fuels, such as low-carbon and non-carbon fuels, is becoming increasingly important, particularly in the transportation sector. Therefore, hydrogen has emerged as a promising fuel for internal combustion engines because it does not emit carbon dioxide. Direct injection is mandatory for hydrogen-based internal combustion engines to mitigate backfires and low energy density. However, there is a lack of measurement of the equivalence ratio methodology because hydrogen has a higher diffusion rate than conventional fuels. The objective of this research is a feasibility study of laser-induced breakdown spectroscopy (LIBs) for measuring the equivalence ratio. The second harmonic ND-YAG laser was implemented to induce the atomic emission of hydrogen via the breakdown phenomenon. Simultaneously, the hydrogen jet structure was visualized in a constant volume vessel using Schlieren imaging. Therefore, the experimental results have both measurement location and equivalence ratio information. High-speed Schlieren imaging indicated a highly contracted jet structure under elevated-ambient-pressure conditions. Meanwhile, the local-rich mixture was detected only when the ambient pressure was high due to jet contraction. By contrast, hydrogen does not exist in the core region of the jet because the nozzle has a hollow cone shape under low-ambient-pressure conditions. According to preliminary experimental results, the direct-injected hydrogen jet can be measured using LIBs. However, there was a clear limitation because only the local point area could be measured using LIBs. Despite this apparent limitation, LIBs can contribute to promoting hydrogen-based internal combustion engines to meet the carbon neutrality target by 2050

Optimal flickering light stimulation for entraining gamma rhythms in older adults

With aging, optimal parameters of flickering light stimulation (FLS) for gamma entrainment may change in the eyes and brain. We investigated the optimal FLS parameters for gamma entrainment in 35 cognitively normal old adults by comparing event-related synchronization (ERS) and spectral Granger causality (sGC) of entrained gamma rhythms between different luminance intensities, colors, and flickering frequencies of FLSs. ERS entrained by 700 cd/m(2) FLS and 32 Hz or 34 Hz FLSs was stronger than that entrained by 400 cd/m(2) at Pz (p < 0.01) and 38 Hz or 40 Hz FLSs, respectively, at both Pz (p < 0.05) and Fz (p < 0.01). Parieto-occipital-to-frontotemporal connectivities of gamma rhythm entrained by 700 cd/m(2) FLS and 32 Hz or 34 Hz FLSs were also stronger than those entrained by 400 cd/m(2) at Pz (p < 0.01) and 38 Hz or 40 Hz FLSs, respectively (p < 0.001). ERS and parieto-occipital-to-frontotemporal connectivities of entrained gamma rhythms did not show significant difference between white and red lights. Adverse effects were comparable between different parameters. In older adults, 700 cd/m(2) FLS at 32 Hz or 34 Hz can entrain a strong gamma rhythm in the whole brain with tolerable adverse effects.N