Miracle-dz/UniFAR-FTUs huggingface dataset:
PicoVO: A Lightweight RGB-D Visual Odometry Targeting Resource-Constrained IoT Devices

Ego-motion estimation with 3D perception using visual odometry (VO) is known to be robust and economical among the existing odometry techniques. However, existing VO solutions are typically both computation intensive and memory intensive, which dramatically inhibits their deployment in IoT platforms such as robotic vehicles and handheld devices mostly equipped with resource-constrained MCU-level processors. To enable real-time and high-quality VO on these scenarios with thrifty resource budgets, we investigate state-of-the-art edge-based VO (EBVO) and propose an optimization framework called PicoVO that can greatly reduce the amount of computation as well as the memory footprint from the perspectives of both algorithm and implementation. First of all, we revisit the key processing stages of EBVO and propose an EBVO-oriented lightweight edge detector in the pre-processing stage, a sparse-to-dense processing scheme in the tracking stage, and a lightweight key-frame management in the post-processing stage. In addition to the algorithmic optimization, we further develop a dedicated quantization scheme particularly for the 3D feature calculation and Levenberg-Marquardt (LM) solver that are critical to the memory footprint and computation requirements of PicoVO. Evaluation on realistic RGB-D benchmark datasets is conducted on NUCLEO-F767ZI equipped with a 216MHz Cortex-M7 MCU and 512KB RAM. It reveals that PicoVO achieves 33fps@320x240 with high tracking precision comparable to state-of-the-art VOs on PC.
Autonomous Flying into Buildings in a Firefighting Scenario

We propose an approach enabling an Unmanned Aerial Vehicle (UAV) to autonomously enter a target building through an open window. We use a fusion of depth camera and 2D Light Detection and Ranging (LiDAR) data for window detection and continuous estimation of its position, orientation, and size. The proposed algorithms are capable of running both with and without available a priori information. The obtained detections are utilized for planning collision-free trajectories through the target window. We use a sensor fusion algorithm for robust altitude estimation from laser rangefinder data while flying over ground with inconsistent elevation. Particular focus is given to the transition between outdoor and indoor environments and vice-versa to achieve the required reliability of UAV state estimation. The proposed approach has been verified in multiple real-world experiments, where the UAV was able to successfully enter and leave the target building both under normal conditions and under decreased visibility conditions in a smoke-filled environment.
Learning-based Inverse Kinematics from Shape as Input for Concentric Tube Continuum Robots

We introduce a methodology to compute the inverse kinematics for concentric tube continuum robots from a desired shape as input. We demonstrate that it is possible to accurately learn joint parameters using neural networks for a discrete point-wise shape representation with different discretization. In comparison to a vanilla numerical method, the learning-based method is preferred in terms of accuracy in joint space and computation. Representing the shape with up to 20 equidistant points, a shape-to-joint inverse kinematics with errors of 2.22° and 1.45 mm is obtained. Further, we extend the shape-to-joint inverse kinematics to image-to-joint inverse kinematics utilizing multi-view images as shape representation. This image-based method achieves errors of 6.02° and 2.76 mm. Both approaches, i.e., shape-to-joint and image-to-joint, result in higher accuracy compared to the learning-based state-of-the-art approach which only considers the tip pose.
Lywal: a Leg-Wheel Transformable Quadruped Robot with Picking up and Transport Functions

This paper introduces a leg-wheel transformable quadruped robot named Lywal which can switch to the leg-mode and the wheel-mode for locomotion, and the claw-mode for picking up and transport functions. First, the mechanical structure of Lywal is designed by using an innovative 2-DoF transformable mechanism. Second, the calculation of kinematics is analyzed in detail. Then, the switching-mode strategy and the mobile control strategies in different modes are designed. Finally, the prototype of Lywal is built. The properties of the mobile modes are analyzed, and the picking-up and transport functions of the claw-mode are verified through physical experiments.
Collective Transport of Unconstrained Objects via Implicit Coordination and Adaptive Compliance

We present a decentralized control algorithm for robots to aid in carrying an unknown load. Coordination occurs solely through sensing of the forces on or movement of the shared load. Robots prevent undesired motion of the load while permitting movement in the task-relevant subspace, and stabilize against unexpected events by a transient decrease in compliance. The algorithm requires no direct communication between agents, and minimal knowledge of the system or task. We demonstrate the approach in simulation using a commercially available compliant robotic platform.
Operational Space Control for Planar PAN–1 Underactuated Manipulators Using Orthogonal Projection and Quadratic Programming

In this paper, we propose an operational space control formulation for a planar N-link underactuated manipulator (PAN–1)1 with a passive first joint subject to actuator constraints (N ⩾ 3), covering both stabilization and tracking tasks. Such underactuated manipulators have an inherent first-order nonholonomic constraint, allowing us to project their dynamics to a space consistent with the nonholonomic constraint. Based on the constrained dynamics, we can design operational space controllers with respect to tasks assuming that all joints of the manipulator are active. Due to underactuation, we design a Quadratic Programming (QP) based controller to minimize the error between the desired torque commands and available motor torques in the null space of the constraint, as well as involve the constraint of motor outputs. The proposed control framework was demonstrated by stabilization and tracking tasks in simulations with both planar PA2 and PA3 manipulators. Furthermore, we verified the controller experimentally using a planar PA2 robot.
An Overconstrained Robotic Leg with Coaxial Quasi-direct Drives for Omni-directional Ground Mobility*

Planar mechanisms dominate modern designs of legged robots with remote actuator placement for robust agility in ground mobility. This paper presents a novel design of robotic leg modules using the Bennett linkage, driven by two coaxially arranged quasi-direct actuators capable of omnidirectional ground locomotion. The Bennett linkage belongs to a family of overconstrained linkages with three-dimensional spatial motion and unparalleled joint axes. We present the first work regarding the design, modeling, and optimization of the Bennett leg module, enabling lateral locomotion, like the crabs, that was not capable with robotic legs designed with common planar mechanisms. We further explored the concept of overconstrained robots, which is a class of advanced robots based on the design reconfiguration of the Bennett leg modules, serving as a potential direction for future research.
Balancing on a Springy Leg

This paper presents a simulation study of the problem of balancing a planar double pendulum in which the lower body (the leg) has been modified to include a spring-loaded passive prismatic joint. Robots of this kind can travel by hopping, and can also stand and balance on a single point. The purpose of this study is to investigate the degree to which a balance controller can cope with the large and rapidly changing forces from the spring. It is shown that good performance can be achieved using an existing balance controller if the spring-loaded joint is instrumented so that its position and velocity can be taken into account when calculating the state variables needed by the balance controller.
Magic Shapes for Validation in SHACL

The Shape Constraint Language (SHACL) was recently standardized by the W3C as a formalism for checking the quality of RDF graphs; we refer to [7] for an introduction. In SHACL, the main problem is to check whether a given RDF graph G validates a SHACL document (C, T ), where C is a set of constraints, also called shapes graph, each associated to a so-called shape name, and T (targets) is a specification of nodes from the data graph which should validate certain shapes from C. For illustration, consider a graph G = {enrolledIn(Ben,C1)} and a SHACL document (C, T ), where C = {Student ↔ ∃enrolledIn.Course}, and T is the shape atom Student(Ben). The constraint states that each Student must be enrolled in some course; Student is a shape name, and enrolledIn and Course are data predicates, i.e., property and class name, respectively. Clearly, G does not validate (C, T ), but the extended graph G′ = G∪{Course(C1)} does. The standard specifies a syntax for expressing SHACL constraints and describes when they are validated by RDF graphs. However, it leaves undefined the semantics of recursive constraints, i.e., constraints that involve cyclic dependencies. To address this, some logic-based proposals to formalize the semantics of full SHACL have emerged recently. Andresel et al. [1] proposed a semantics based on the stable models semantics for logic programs, stricter than the semantics based on classical logic due to Corman el al. [4]. Both semantics coincide with the official recommendation for non-recursive constraints, and unfortunately, the validation problem is NP-complete under both. To make SHACL truly useful and facilitate its adoption, we need automated tools that efficiently implement validation and scale well in the presence of large RDF graphs and sets of constraints. There are already significant efforts in this direction for fragments of SHACL [3, 6]. Shacl2Sparql [3] is a SHACL validation engine that checks conformance of RDF graphs with SHACL constraints by evaluating SPARQL queries against the data, which optimzes the order in which shapes are processed. Further optimization techniques are implemented in Trav-Shacl [6]. However, these works focus on tractable fragments of SHACL. They do not handle unrestricted interaction of recursion and negation in SHACL constraints, which calls for verifying whether there exists some global assignment of shapes to nodes in the graph that is consistent with all the constraints. This cannot be easily done using top-down approaches implemented in existing validators. To our knowledge, the only implementation that validates full SHACL
Embedding Metadata-Enriched Graphs

This paper presents an on-going research where we studythe problem of embedding meta-data enriched graphs, with a focus onknowledge graphs in a vector space with transformer based deep neuralnetworks. Experimentally, we compare ceteris paribus the performance ofa transformer-based model with other non-transformer approaches. Dueto their recent success in natural language processing we hypothesizethat the former is superior in performance. We test this hypothesizesby comparing the performance of transformer embeddings with non-transformer embeddings on different downstream tasks. Our researchmight contribute to a better understanding of how random walks in-fluence the learning of features, which might be useful in the design ofdeep learning architectures for graphs when the input is generated withrandom walks.
WikidataComplete: KnowledgeGraph Completion using Question-Answering

In this demonstration, we showcase WikidataComplete , a KG completion system for Wikidata based on Question Answering (QA). We showcase (https://wikidatacomplete.org) that it is possible to leverage Question Answering to extract new facts from Wikipedia and jointly provide explanations for these extracted facts. Providing explanations has two advantage: first it helps human annotators to verify if the generated facts are correct, second it allows ingesting the approved facts in the KG together with provenance information. Ultimately, we measure the accuracy of such a system for the first time on a real scenario including human annotations.
Healthy Food Recommendation and Explanation Generation using a Semantically-Enabled Framework

Recommender systems are an important service utilized in a wide variety of applications, but they rarely explain the processes and data used to generate the ﬁnal recommendation. Furthermore, no convenient software resource exists to help developers create recommender systems that use a variety of strategies, such as knowledge-driven recommendation processes. We present a Python framework to support the development of recommender systems with an emphasis on using data sources containing rich semantics and providing explanations for each step involved in producing the recommendation. We illustrate this through an example food recommendation system developed using the framework.
R4R: Template-based REST API Framework for RDF Knowledge Graphs

Knowledge graphs (KGs) are increasingly being used to make structured information available on the Web, by means of REST APIs and/or SPARQL endpoints. In many cases, these REST APIs are generated on top of the SPARQL endpoints, using existing technology approaches that are based on proprietary conﬁguration ﬁles or ontologies to create the APIs. These approaches may impose content-based or structural constraints when composing Web resources. To relax these constraints we propose R4R, a more ﬂexible solution based on Web standards and REST principles that creates and publishes customizable APIs exposing Web resources from SPARQL queries organized in ﬁle system directories. R4R features include individual and nested resources, pagi-nated queries, optional ﬁelds, web authentication, query parameters and sorting lists.
Is there data leakage in protein-protein interaction prediction using knowledge graphs?

There is a high potential for data leakage in biomedical machine learning applications since biomedical data resources share, reuse and import data from each other routinely. We have investigated potential data leakage in the prediction of protein-protein interactions using the Gene Ontology knowledge graph, by comparing the performance of models trained and tested on the same versions of data versus training on archived data and predicting only for newly discovered protein interactions. Our results were not able to detect an inﬂuence of data leakage, indicating that if this problem exists, its magnitude is not aﬀecting the performance of knowledge graph-based protein interaction predictions.
Biomimetic Operational Space Control for Musculoskeletal Humanoid Optimizing Across Muscle Activation and Joint Nullspace

We have implemented a force-based operational space controller on a physical musculoskeletal humanoid robot arm. The controller calculates muscle activations based on a biomimetic Hill-type muscle model. We propose a method to include the joint torque nullspace in the optimization process, which enables the robot to exploit the nullspace to gradually lower its overall muscle activation. We have verified in experiments that it can react compliantly to external disturbances while retaining its operational space task.
A 93GHz Slot Antenna Array for Millimeter-Wave Proximity Sensing

A millimeter-wave slot antenna array operating at 93GHz is presented. The linear array antenna consists of 34 slots, and is feed by WR10 waveguide in the center. The measured bandwidth is 2.5GHz(S11<-10dB), and the 3dB beam width for E plane and H plane are 110° and 3.2° respectively. Meanwhile, a -19.2dB side-lobe level is achieved. The proposed compact antenna can be used in the field of millimeter-wave proximity sensing and other wireless applications.
On the Application of Lifetime-Equivalent Defect Densities on Solar Cell Level

The concept of lifetime-equivalent defect density based on lifetime measurements as a measure of electrically active defect density is extended to solar cells using Suns-V$_{\mathrm{oc}}$ measurements. It is found to be superior to previous approaches, and offers defect-specific injection-dependent information. However, the correct application of the presented method requires some knowledge of or reasonable assumptions on sample properties and measurement conditions, in particular, effective intrinsic carrier concentration, doping level, and measurement temperature. Furthermore, it is found that samples with high effective excess carrier lifetime can be susceptible to nonequilibrium related issues when the measurement duration is too short compared with lifetime. The use of series-resistance affected current-voltage curves is found to be error-prone.
Beelines: Motion Prediction Metrics for Self-Driving Safety and Comfort

The commonly used metrics for motion prediction do not correlate well with a self-driving vehicle’s system-level performance. The most common metrics are average displacement error (ADE) and final displacement error (FDE), which omit many features, making them poor self-driving performance indicators. Since high-fidelity simulations and track testing can be resource-intensive, the use of prediction metrics better correlated with full-system behavior allows for swifter iteration cycles. In this paper, we offer a conceptual framework for prediction evaluation highly specific to self-driving. We propose two complementary metrics that quantify the effects of motion prediction on safety (related to recall) and comfort (related to precision). Using a simulator, we demonstrate that our safety metric has a significantly better signal-to-noise ratio than displacement error in identifying unsafe events.
3D Printed Electromagnetic Absorber Built with Conductive Carbon-filled Filament

This article presents a method for manufacturing a frequency selective surface (FSS) using the 3D printing technology, including material characterization, design, fabrication, and evaluation. The FSS design is based on a three-layer structure. The first layer is a 3D printed lossy material, the second layer is an air gap, and the third layer is a ground plane. By changing the geometrical parameters of the 3D printed layer, it is possible to tune the frequency of the absorption band of the FSS.
Chance Constrained Scheduling and Pricing for Multi-Service Battery Energy Storage

This paper studies optimal day-ahead scheduling of grid-connected batteries that simultaneously provide three services: 1) load shifting, 2) real-time balancing, and 3) primary frequency control (PFC). The uncertainties of load and frequency are incorporated in the cost-minimizing scheduling problem via chance constraints. The resulting chance-constrained problem is then reformulated into a mixed-integer second-order cone program (MISOCP) that can be solved by commercial solvers. However, it is computationally formidable to obtain the globally optimal solution to the MISOCP due to the big problem size. To obtain a suboptimal solution quickly, a heuristic based on penalty alternating direction method (PADM) is developed to solve the MISOCP. Fixing the integer solution returned by the heuristic, we adopt the duality of the second-order cone program (SOCP) to price the three services in the local market. Theoretical analysis of the market equilibrium, individual rationality, and balanced budget is given. Real-world data of load, frequency, and price in the French grid is used in simulation. The results show that the proposed heuristic is computationally efficient, and the pricing results can guarantee a positive utility for each of the batteries, incentivizing them to provide services.
A new approach for Video Action Recognition: CSP-based filtering for video to image transformation

In this paper we report on the design of a pipeline involving Common Spatial Patterns (CSP), a signal processing approach commonly used in the field of electroencephalography (EEG), matrix representation of features and image classification to categorize videos taken by a humanoid robot. The ultimate goal is to endow the robot with action recognition capabilities for a more natural social interaction. Summarizing, we apply the CSP algorithm to a set of signals obtained for each video by extracting skeleton joints of the person performing the action. From the transformed signals a summary image is obtained for each video, and these images are then classified using two different approaches; global visual descriptors and convolutional neural networks. The presented approach has been tested on two data sets that represent two scenarios with common characteristics. The first one is a data set with 46 individuals performing 6 different actions. In order to create the group of signals of each video, OpenPose has been used to extract the skeleton joints of the person performing the actions. The second data set is an Argentinian Sign Language data set (LSA64) from which the signs performed using just the right hand have been used. In this case the joint signals have been obtained using MediaPipe. The results obtained with the presented method have been compared with a Long Short-Term Memory (LSTM) method, achieving promising results.
Stair Climbing Capability-Based Dimensional Synthesis for the Multi-legged Robot

Staircase is a typical obstacle for the legged robot to overcome in buildings. This paper studies the stair climbing capability-based dimensional synthesis for a hexapod legged robot, i.e., exploring how to determine the leg length and the longitudinal body length concerning the target staircase in the mechanical design stage. In climbing a staircase, leg-staircase interference is one of the predominant issues. The three possible interference cases are illustrated in detail with a 2-DOF (degree of freedom) leg mechanism and the staircase size, based on the predefined tripod gait sequence. The mathematical relationships between the leg length, longitudinal body length, and the target staircase size are derived. The leg length and the body length are finally determined with the target staircase size. The virtual simulations and prototype experiments verify the effectiveness of the dimensional synthesis for the hexapod robot.
Simulated Verification for a Finite Rate of Innovation Method Applied to Terahertz Signals

Methods utilizing finite rate of innovation theory have been employed to achieve low sampling rates in ultrasound experiments. Here, we apply this theory to create a terahertz specific method to gain the benefits of faster signal processing speeds and data acquisition compared with current standard processing methods. We verify our method by simulating a THz like signal, adding Gaussian noise, using a low sampling rate, processing it through our code and then comparing the reconstructed output to the original simulated signal to find close agreement.