Torc Robotics (United States)

Monte Carlo (MC) methods estimate high-dimensional integrals by computing sample averages at independent and identically distributed (IID) random points.Quasi-Monte Carlo (QMC) methods replace IID samples with low-discrepancy (LD) sequences which more uniformly cover the integration domain, leading to faster convergence and reduced computational requirements.Figure 1 visualizes IID and LD sequences.

Words like robotaxis and shuttles are likely to come up when talking about autonomous vehicles. If you look at the subject in more detail, it becomes clear that freightFreight transportation transportation, the engine and driver of the economy, also has enormous potential. This section describes how freight transportation works, where the potential lies and it explains how this can be exploited using the two very popular use cases ‘middle mile’ and ‘last mile delivery’.

<div>Functional safety forms an important aspect in the design of systems. Its emphasis on the automotive industry has evolved significantly over the years. Till date many methods have been developed to get appropriate fault tree analysis (FTA) for various scenarios and features pertaining to autonomous driving. This article is an attempt to explore the scope of using generative artificial intelligence (GenAI) FTA with the use case of malfunction for the LIDAR sensor in mind. We explore various available open source large language models (LLM) models and then dive deep into one of them to study its responses and provide our analysis. Although the article does not solve the entire problem but has given some guidance or thoughts/results to explore the possibility to train existing LLM through prompt engineering for FTA for any autonomy use case aided with PlantUML tool.</div>

<div class="section abstract"><div class="htmlview paragraph">Several challenges remain in deploying Machine Learning (ML) into safety critical applications. We introduce a safe machine learning approach tailored for safety-critical industries including automotive, autonomous vehicles, defense and security, healthcare, pharmaceuticals, manufacturing and industrial robotics, warehouse distribution, and aerospace. Aiming to fill a perceived gap within Artificial Intelligence and ML standards, the described approach integrates ML best practices with the proven Process Failure Mode & Effects Analysis (PFMEA) approach to create a robust ML pipeline. The solution views ML development holistically as a value-add, feedback process rather than the resulting model itself. By applying PFMEA, the approach systematically identifies, prioritizes, and mitigates risks throughout the ML development pipeline. The paper outlines each step of a typical pipeline, highlighting potential failure points and tailoring known best practices to minimize identified risks. As an additional contribution, a populated ML FMEA Template is provided. The ML FMEA captures the method into a modified PFMEA framework that connects each pipeline step with failure causes with known mitigations. The ML FMEA Template is designed as a handy tool for development teams to identify, manage, and communicate risk and to enable risk transparency with safety experts.</div></div>

Lidar has become a cornerstone sensing modality for 3D vision, especially for large outdoor scenarios and autonomous driving. Conventional lidar sensors are capable of providing centimeter-accurate distance information by emitting laser pulses into a scene and measuring the timeof-flight (ToF) of the reflection. However, the polarization of the received light that depends on the surface orientation and material properties is usually not considered. As such, the polarization modality has the potential to improve scene reconstruction beyond distance measurements. In this work, we introduce a novel long-range polarization wavefront lidar sensor (PolLidar) that modulates the polarization of the emitted and received light. Departing from conventional lidar sensors, PolLidar allows access to the raw time-resolved polarimetric wavefronts. We leverage polarimetric wavefronts to estimate normals, distance, and material properties in outdoor scenarios with a novel learned reconstruction method. To train and evaluate the method, we introduce a simulated and real-world long-range dataset with paired raw lidar data, ground truth distance, and normal maps. We find that the proposed method improves normal and distance reconstruction by 53% mean angular error and 41% mean absolute error compared to existing shape-from-polarization (SfP) and ToF methods. Code and data are open-sourced here 1 .

This manuscript is a concise review of a selection of key concepts concering _rythmic oscillations in proteins to human cognition_. All matter has an associated frequency, characterized by υ_ = (mc2)/h _(derived from the equivalency _E = mc2 + pc_, and _E = hυ_). In large organic macromolecules and biopolymers of the living system, there are multiple vibrational modes and oscillatory frequencies, the resonances of which can facilitate molecular recognition, coupling with field modes, electromagnetic and Van der Waals interactions. Via harmonic rhythmic oscillations and resonances nanosecond-scale intermolecular interactions are highly coordinated to orchestrate the myriad complex biochemistry pathways of the cell. Importantly, this field-like interaction is integral in information processing and exchange occurring at the molecular level that underlie cellular intelligence, network intelligence, and perhaps even sentience and consciousness. We will review here the _resonant recognition model _of Irena Cosic and the scale-invariant acoustic information code of a superfluid quantum space described by Meijer.

Here we describe an integrated model for scheduled care (the ‘cluster clinic’). Following a pilot in April 2018, cluster clinics were established across Aberdeen City from April 2019 but not the area surrounding Aberdeen (ie, Aberdeenshire). There were 2360 referrals in 2017/2018 (pre-cluster clinic), and 2615 in 2019/2020 (post-Aberdeen City cluster clinics). The proportions of referrals from City practices seen pre-cluster and post-cluster were 72% and 56%, respectively, and from Shire practices the corresponding proportions were 70% and 65%. The cluster clinic received positive feedback from parents and referring clinicians and was not associated with increased ‘missed diagnoses’ compared with business as usual clinic. The cluster clinic model is a realistic and effective method to deliver integrated scheduled care for children.

As hardware platforms mature and evolve to contain higher compute capacity, Small Unmanned Aerial Systems (sUAS) are increasingly capable of operating as fully-integrated, cooperative inspection systems. A variety of lightweight sensing payloads are emerging for efficient multi-modal data collection. Deep learning algorithms applied to this sensor data significantly reduce the burden on system operators and enable the fusion of data from multiple sources for enhanced decision making. The Air Force Civil Engineer Center (AFCEC) and TORC Robotics are developing a Rapid Airfield Damage Assessment System (RADAS) that uses simultaneous data streams from multiple sUAS and ground sensors for computer-aided condition assessment and planning of airfield repair. Operators, aided by intelligent algorithms, remotely monitor incoming data and software tools to identify a Minimum Airfield Operating Surface (MAOS). Recent developments by AFCEC and TORC use deep learning algorithms to eliminate the bottleneck of human-in-the-loop interpretation of multiple simultaneous data sources. These advances provide a supervised autonomous workflow in: (a) identification of damages from multiple incoming sUAS video streams, (b) automated tasking of decisions based on that data, and (c) adjustment of decisions based on additional incoming information. Preliminary results demonstrate significant reduction in airfield assessment time, increased assessment accuracy, and remove humans from danger during the inspection process. This work is part of the RADAS program funded by the Air Force Civil Engineering Center (AFCEC).

Humanoid robotic manipulation in unstructured environments is a challenging problem. Limited perception, communications and environmental constraints present challenges that prevent fully autonomous or purely teleoperated robots from reliably interacting with their environment. In order to achieve higher reliability in manipulation we present an approach involving remote human supervision. Strengths from both human operator and humanoid robot are leveraged through a user interface that allows the operator to perceive the remote environment through an aggregated worldmodel based on onboard sensing, while the robot can efficiently receive perceptual and semantic information from the operator. A template based manipulation approach has been successfully applied to the Atlas humanoid robot; we show real world footage of the results obtained in the DARPA Robotics Challenge Trials 2013.

<div class="section abstract"><div class="htmlview paragraph">Full-toroidal traction drives (IVT, TCVT & TVAD) have demonstrated a significant fuel economy, emissions and cost benefits across a diverse range of applications. However, increasing emphasis is being placed on power density and endurance limits, hence additional demands are being placed on the ability of the variator elements and traction fluid to withstand the heavily loaded rolling contact fatigue conditions within the variator. This paper describes the experimental work done to demonstrate the high temperature durability of traction fluid and variator disc and roller material. In addition, the latest fatigue endurance limit testing results are presented which provide further evidence of traction fluid ‘fill for life’</div></div>