Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Landing Stabilization of Humanoid Robot on Uneven Terrain via Admittance Control and Heel Strike Motion

This paper addresses robust landing stabilization in humanoid locomotion on uneven terrain. The core idea is to find a configuration of the robot that results in small impulsive force when an unexpected obstacle is encountered, and to adjust post-contact reference for swing foot with which the pose of the foot is stabilized on the obstacle. This can be achieved by walking with heel strike motion (validated by the impact map analysis) and by employing hybrid admittance control combining the admittance control with reset of post-contact reference, embedded into the momentum-based whole-body control framework. The validity of the proposed algorithm is verified by simulation with a physics engine.
IMU/Vehicle Calibration and Integrated Localization for Autonomous Driving

The localization system, which outputs vehicle position, velocity, and attitude, is one of the fundamental components in the autonomous driving vehicle. The global pose is not only used for the planning and control system, but also an important reference for the cloud source-based HD Map building and updating. The accuracy, availability, and reliability are key requirements for the localization system to ensure that the whole system runs smoothly and efficiently.IMU/Vehicle extrinsic calibration is one of the primary jobs that should be addressed. Due to the observability issue, the IMU/vehicle relative roll cannot be calibrated by the traditional maneuver-based calibration method. In this paper, we solve this issue with the proposed Multiple Orientation-based Vehicle/IMU Extrinsic Calibration (MOVIE-Cali) method, which is evaluated by Monte Carlo simulations and experiments.When the vehicle is cornering or making a U-turn, the sideslip of the tires will have negative influence on the localization system which uses Non-Holonomic Constraints (NHC)/Wheel speed sensor measurement in the model. We derive a sideslip angle model and propose an online slip parameter calibration and compensation method to improve the localization accuracy. The performance of proposed method has been evaluated by the vehicle tests.
A Peg-in-hole Task Strategy for Holes in Concrete

A method that enables an industrial robot to accomplish the peg-in-hole task for holes in concrete is proposed. The proposed method involves slightly detaching the peg from the wall, when moving between search positions, to avoid the negative influence of the concrete’s high friction coefficient. It uses a deep neural network (DNN), trained via reinforcement learning, to effectively find holes with variable shape and surface finish (due to the brittle nature of concrete) without analytical modeling or control parameter tuning. The method uses displacement of the peg toward the wall surface, in addition to force and torque, as one of the inputs of the DNN. Since the displacement increases as the peg gets closer to the hole (due to the chamfered shape of holes in concrete), it is a useful parameter for inputting in the DNN. The proposed method was evaluated by training the DNN on a hole 500 times and attempting to find 12 unknown holes. The results of the evaluation show the DNN enabled a robot to find the unknown holes with average success rate of 96.1% and average execution time of 12.5 seconds. Additional evaluations with random initial positions and a different type of peg demonstrate the trained DNN can generalize well to different conditions. Analyses of the influence of the peg displacement input showed the success rate of the DNN is increased by utilizing this parameter. These results validate the proposed method in terms of its effectiveness and applicability to the construction industry.
MRSaiFE: An AI-Based Approach Towards the Real-Time Prediction of Specific Absorption Rate

The purpose of this study is to investigate feasibility of estimating the specific absorption rate (SAR) in MRI in real time. To this goal, SAR maps are predicted from 3T- and 7T-simulated magnetic resonance (MR) images in 10 realistic human body models via a convolutional neural network. Two-dimensional (2-D) U-Net architectures with varying contraction layers and different convolutional filters were designed to estimate the SAR distribution in realistic body models. Sim4Life (ZMT, Switzerland) was used to create simulated anatomical images and SAR maps at 3T and 7T imaging frequencies for Duke, Ella, Charlie, and Pregnant Women (at 3, 7, and 9 month gestational stages) body models. Mean squared error (MSE) was used as the cost function and the structural similarity index (SSIM) was reported. A 2-D U-Net with 4 contracting (and 4 expanding) layers and 64 convolutional filters at the initial stage showed the best compromise to estimate SAR distributions. Adam optimizer outperformed stochastic gradient descent (SGD) for all cases with an average SSIM of <inline-formula> <tex-math notation="LaTeX">$90.5 \mp 3.6$ </tex-math></inline-formula> % and an average MSE of <inline-formula> <tex-math notation="LaTeX">$0.7 \mp 0.6$ </tex-math></inline-formula>% for head images at 7T, and an SSIM of ><inline-formula> <tex-math notation="LaTeX">$85.1 \mp 6.2$ </tex-math></inline-formula> % and an MSE of <inline-formula> <tex-math notation="LaTeX">$0.4 \mp 0.4$ </tex-math></inline-formula>% for 3T body imaging. Algorithms estimated the SAR maps for <inline-formula> <tex-math notation="LaTeX">$224\times 224$ </tex-math></inline-formula> slices under 30 ms. The proposed methodology shows promise to predict real-time SAR in clinical imaging settings without using extra mapping techniques or patient-specific calibrations.
Design and soft-landing control of a six-legged mobile repetitive lander for lunar exploration

The autonomous robots consisting of an immovable lander and a rover are widely deployed to explore extraterrestrial planets. However, these robots have two main limitations: (1) the separate design for lander and rover respectively results in heavy mass and big volume of the whole system, which increases the launching cost sharply; (2) the rover’s detection area has to be restricted to the vicinity of the immovable lander. To overcome these problems, we designed a novel six-legged mobile repetitive lander called "HexaMRL", which integrates the functions of both lander and rover, including folding, deploying, repetitive soft-landing, and walking. A hybrid compliant mechanism taking advantages of both active and passive compliances was adopted on its leg. An integrated drive unit (IDU) was utilized to imitate the dynamics of a spring and a damper to absorb the landing impact energy, while the structure remains intact. Moreover, a control method based on state machine for soft-landing on the Moon was proposed. HexaMRL achieved repetitive soft-landing on a 5-DoF lunar gravity testing platform (5-DoF-LGTP) with a vertical landing velocity of 1.9 m/s and a payload of 140 kg. The drive torque safety margin is improved by 23.4%p based on the hybrid compliant leg comparing with the standalone active compliant leg.
Thrust Enhancement of Wave-driven Unmanned Surface Vehicle by using Asymmetric Foil

In the Wave-driven unmanned surface vehicles (WUSVs), oscillating-foils are the most straightforward and widely used wave energy conversion mechanism. In this paper, a kind of novel asymmetric foil is proposed, which improves the wave energy-converting efficiency to provide a more significant thrust in every wave cycle. We break down the movement of the foils in the wave and build the corresponding kinetic model to analyze their working effectiveness numerically. Through computational fluid dynamic (CFD) simulations, we determine the optimal values of critical parameters of the foils, which are suitable for a wide range of wave conditions. The thrust enhancement of the asymmetric foil is verified in both CFD simulations and hydrodynamic experiments, and the result shows a similar enhancement trend. Comparing with the traditional symmetric foil, our asymmetric foil can provide at least 13.75% more thrust to the WUSVs.
Boosting Information Extraction through Semantic Technologies: The KIDs use case at CONSOB

In this paper we report on the initial results of a project concerning the integration of Semantic Technologies with Information Extraction (IE) techniques, jointly carried out by Sapienza University of Rome and C ONSOB (Commissione Nazionale per la Societ ` a e la Borsa), the Italian public authority responsible for regulating the securities market. The use case. In the EU, the creators of ﬁnancial products (a.k.a. ﬁnancial manufacturers) are obliged by law 3 to make information related to so-called PRIIPs (Packaged Retail Investment and Insurance-based Investments Products) publicly available. The NCAs (National Competent Authorities) have supervisory duties on such products, so that they can be safely placed on the respective national markets. The legislation requires information about PRIIPs to be communicated to NCAs through documents called KIDs (Key Information Documents). In the practice, this means that features to be checked are cast into text reports, typically formatted as pdf ﬁles, and extracting structured data from them (to bootstrap control activities), is actually in charge to the authority (In Italy, C ONSOB ). Due to the massive amount of documents to be analyzed (e.g., ∼ 700.000 KIDs received by C ONSOB in 2019, more than 1 million in 2020), this process cannot be carried out manually, but still it is only partially automated to date. Objectives. Our main aim is thus to develop a solution to streamline the extraction process and reduce as much as possible (ideally eliminate) the need of manual intervention, still guaranteeing very high accuracy. At the same time, such solution should return a data structure providing a due account of the
A Model-Driven Approach for Formally Verifying SysML-Based Dynamic Software Architectures (short paper)

The critical nature of many complex software-intensive systems requires formal architecture descriptions towards better supporting automated architectural analysis regarding correctness properties. Due to the challenges of adopting formal approaches, many architects have preferred using notations such as UML, SysML, and their derivatives to describe the structure and behavior of software architectures. However, these semi-formal notations have limitations regarding the sought support for architectural analysis, particularly formal verification. This Ph.D. research investigates how to conciliate formal support and SysML-based architecture descriptions, to enable the formal verification of dynamic software architectures. The main contribution is proposing a model-driven approach that: (i) provides formal semantics to a SysML-based architectural language, SysADL, by transforming SysADL architecture descriptions in specifications expressed in π -ADL, a well-founded theoretically language based on the higher-order typed π -calculus, and (ii) enables the formal verification of properties for dynamic architectures. These facilities are integrated into an environment that facilitates modeling SysML architectures and formally verifying them.
Leveraging Knowledge Graph and DeepNER to Improve UoM Handling in Search

Understanding Unit of Measurements (UoM) is critical to e-commerce search engines. It is a challenging problem since numeric values and unit symbols are typically treated as regular text tokens. In this paper, we introduce a framework that utilizes Knowledge Graph (KG) and deep learning based Named Entity Recognition (NER) to provide better semantic understanding of UoMs.
Hierarchical Expected Answer Type Classification for Question Answering

To know what a user’s question is about is a crucial step in the Question Answering (QA) process. Thus, the Expected Answer Type (EAT) of a question enables to signiﬁcantly narrow down the search ﬁeld and improve the QA quality. In this paper, we present a Web user interface (UI) and a RESTful API for the hierarchical EAT classiﬁcation over DBpedia. The provided functionality enables end-users to get the EAT predictions for 104 languages, see the conﬁdence of the prediction, and leave feedback. In addition, the API enables researchers and developers to integrate the EAT classiﬁcation into their systems.
Reranking Triples by Leveraging Text Descriptions for Link Prediction

Link prediction task intends to complete Knowledge Graphs (KGs) which are always far from complete. Textual descriptions of entities in KG provide additional information that may not be explicitly represented in the structured part of the KG. Current methods aim to learn the representation of KG and predict missing links by utilizing structured and textual information. In this poster, we propose a novel rerank method that introduces the natural language inference task to leverage textual information of entities in a different way. The experiment demonstrates that our rerank method improves the quality of link prediction.
Biomedical Entity Normalization based on Pre-trained Model with Enhanced Information

Biomedical entity normalization, which links entity mentions in biomedical texts to their corresponding standard concepts in a knowledge base(KB) or an ontology is an important task in biomedical text mining. A prevalent solution is to generate the most similar concepts, and then rank those concepts with semantic models. Herein, to improve the performance of candidate concepts ranking for entity normalization, we rank the candidates by ﬁne-tuning the domain-speciﬁc pre-trained BioBERT model and enhancing the representation information with entity mentions and candidates. We have achieved signiﬁcant improvement over the state-of-the-art method on the Bacteria Biotope data of BioNLP-OST19 1 .
Model-based Approach for Automatic Generation of Hardware Architectures for Robotics

Increasingly complex robotic platforms incorporate heterogeneous sensors and actuators. They are usually coupled with embedded computers but relying on software solutions not entirely suited for processing large amount of data concurrently and fast enough to keep real-time constrains. FPGAs are ideal candidates to enhance those systems computing capabilities while still being programmable. However, it is cumbersome to manually incorporate them into new or existing systems because providing accelerators with a specific integration capability limits their applicability. This work presents an approach to integrate multiple hardware accelerators into a robotic system. A model-based toolchain automatically generates the necessary hardware components (VHDL modules) from existing message specifications to exchange data with the accelerators. The goal is to seamlessly replace software components with FPGA-based ones while retaining the same communication interface. Instead of writing several hundred lines of VHDL, a dozen input specification lines are sufficient with our approach. The results are validated through an evaluation of all message specifications included in the latest Robot Operating System (ROS) versions. The 2295 messages evaluated show the robustness in our capabilities to support arbitrarily large ROS messages types, multiple data types, and nested messages. Moreover, our approach facilitates the extension from ROS1 to provide support for ROS2 easily. Finally, two use cases are shown to prove the feasibility of our approach on real applications. The first one incorporates a High Level Synthesis (HLS) hardware accelerator for image processing into an existing software architecture. The second one consists of a fully FPGA-based mobile platform with ROS features incorporated.
CNN-based Ego-Motion Estimation for Fast MAV Maneuve

In the field of visual ego-motion estimation for Micro Air Vehicles (MAVs), fast maneuvers stay challenging mainly because of the big visual disparity and motion blur. In the pursuit of higher robustness, we study convolutional neural networks (CNNs) that predict the relative pose between subsequent images from a fast-moving monocular camera facing a planar scene. Aided by the Inertial Measurement Unit (IMU), we mainly focus on translational motion. The networks we study have similar small model sizes (around 1.35MB) and high inference speeds (around 10 milliseconds on a mobile GPU). Images for training and testing have realistic motion blur. Departing from a network framework that iteratively warps the first image to match the second with cascaded network blocks, we study different network architectures and training strategies. Simulated datasets and a self-collected MAV flight dataset are used for evaluation. The proposed setup shows better accuracy over existing networks and traditional feature-point-based methods during fast maneuvers. Moreover, self-supervised learning outperforms supervised learning. Videos and open-sourced code are available at https://github. com/tudelft/PoseNet_Planar
Fully Automatic Operation Algorithm of Urban Rail Train Based on RBFNN Position Output Constrained Robust Adaptive Control

High parking accuracy, comfort and stability, and fast response speed are important indicators to measure the control performance of a fully automatic operation system. In this paper, aiming at the problem of low accuracy of the fully automatic operation control of urban rail trains, a radial basis function neural network position output-constrained robust adaptive control algorithm based on train operation curve tracking is proposed. Firstly, on the basis of the mechanism of motion mechanics, the nonlinear dynamic model of train motion is established. Then, RBFNN is used to adaptively approximate and compensate for the additional resistance and unknown interference of the train model, and the basic resistance parameter adaptive mechanism is introduced to enhance the anti-interference ability and adaptability of the control system. Lastly, on the basis of the RBFNN position output-constrained robust adaptive control technology, the train can track the desired operation curve, thereby achieving the smooth operation between stations and accurate stopping. The simulation results show that the position output-constrained robust adaptive control algorithm based on RBFNN has good robustness and adaptability. In the case of system parameter uncertainty and external disturbance, the control system can ensure high-precision control and improve the ride comfort.
MPPT Control Paradigms for PMSG-WECS: A Synergistic Control Strategy with Gain-Scheduled Sliding Mode Observer

The wind energy conversion system (WECS) frequently operates under highly stochastic and unpredictable wind speed. Thus, the maximum power (MP) extraction, in such unpredictable scenarios, becomes a very appealing control objective. This paper focuses on the extraction of MP from a variable-speed WECS, which further drives a permanent magnet synchronous generator (PMSG). At the first stage, the dynamical model of PMSG is converted into Bronwsky form, which is comprised of both visible and internal dynamics. The first-order internal dynamics are proved stable, i.e., the system is in the minimum phase. The control of the second-order visible dynamics, to track a varying profile of the wind speed, is the main consideration. This job is accomplished via Backstepping-based robust Sliding Mode Control (SMC) strategy. Since the conventional SMC suffers from the inherited chattering issue, thus, the discontinuous control component in the SMC scheme is replaced with super-twisting and real-twisting control laws. In addition, the immeasurable states’ information is estimated via gain-scheduled sliding mode observer. The overall closed-loop stability is ensured by analysing the quasi-linear form, which supports the separation principle. The theoretical claims are authenticated via simulation results, which are performed in Matlab/Simulink environment. Besides, a comparative analysis is carried out with the standard literature results, which quite obviously outshines the investigated control approaches in terms of varying wind profile tracking and the corresponding control input.
The Lay of the Land: Data Visualizations of the Language Data and Domains of Wikidata

Wikidata is a well-known collaborative knowledge graph containing multilingual data for hundreds of language locales, from Arabic (ar) to Zulu (zu). However, research on the state of language localization and the full extent of the topics that currently exist on Wikidata has been relatively limited. This paper presents data visualization in the form of various heatmaps resulting from machine learning experiments using BERT embeddings, t-SNE, and K-means clustering that show the kinds of topics, or domains, in Wikidata and the frequency of multilingual data across such domains. Such heatmaps are useful for identifying domains where language data is lacking and should be populated. A representative heatmap is also presented that shows the localization across domains for the top 30 languages geographically from west to east. Finally, future work in this space is also considered.
Kinematic Stability based AFG-RRT* Path Planning for Cable-Driven Parallel Robots †

Motion planning for Cable-Driven Parallel Robots (CDPRs) is a challenging task due to various restrictions on cable tensions, collisions and obstacle avoidance. The presented work aims at proposing an optimal path planning strategy in order to both maximize the wrench capability and the dexterity of the robot in a cluttered environment. First, an asymptoticallyoptimal path finding method based on a variant of rapidly exploring random trees (RRT) is implemented along with the GilbertJohnsonKeerthi (GJK) algorithm to account for the collision detections. Then, a goal biased Artificial Field Guide (AFG) is employed to reduce convergence time and ensure directional exploration. Finally, a post-processing algorithm is added to get a short and smooth resultant path by fitting appropriate splines. The proposed path planning strategy is analyzed and demonstrated on a simulated and experimental setup of a six-DOF spatial CDPR.
Efficient Haptic Rendering of Regolith

Driven by the need for physically accurate and haptically convincing models of lunar and planetary regolith for model-mediated teleoperation in space, we present an approach to modelling regolith in an efficient yet realistic way. Model parameters are derived from physical characteristics of the regolith, to render regoliths with different density profiles, cohesion, internal friction and in different gravitational fields. Users could distinguish between changes in parameters– specifically friction and gravity field–and also gave qualitative feedback relevant to modelling regolith. We discuss the challenges in haptically rendering soils and the next steps.