Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Sampling-based Next-Best-View Exploration Algorithm for a MAV

In this work, we present a new exploration algorithm for Micro Aerial Vehicles (MAVs). The planner uses a combination of Next-Best-View (NBV) sampling and frontier-based approaches to reduce the impact of finding unexplored areas in large scenarios. For each sampled point, the yaw angle is optimized to maximize the potential gain for mapping. The gain is expressed as a ratio between the exploration objective and the time it would take to reach the pose, thus, balancing the nearby exploration and global coverage. We reduce the gain computation bottleneck in the sampling strategy by managing a dual-map with different resolutions. The planner maintains a history graph, with nodes that indicate regions of interest for map expansion. We demonstrate the abilities of the proposed algorithm with simulations and real-world experiments. The results outperform state-of-the-art methods in exploration time and computational cost.
Grasp Detection for Robot to Human Handovers Using Capacitive Sensors

As it happens, despite yet unmatched by robots perception and motor skills humans drop objects during handover because of false grasp detection and early release. Accordingly, the fluent robot-human handover is still an open challenge. This paper presents an approach to a natural robot to human handover using Capacitive Proximity Sensor (CPS) for robust grasp detection and release trigger. We propose an experimental setup for the evaluation using a collaborative robot, an eye-in-hand depth camera, and CPS integrated into the gripper. Three grasp detection methods were implemented and an object release was triggered based on torque-sensing, capacitive sensing, and the combination of both. Finally, a user study was designed and conducted, indicating that the capacitive method is the most preferred type with the shortest human idle time and the highest fluency ratings.
Mecanum Crank: A Novel Omni-Directional Vehicle Using Crank Leg

A vehicle is expected to exhibit omni-directional locomotion capability to provide improved rough terrain vehicle functionality. Generally, rough terrain vehicles are not holonomic and cannot travel in the lateral direction, whereas typical omni-directional vehicles have difficulty in traveling on rough terrain. This paper proposes installing a crank leg for a Mecanum-wheeled vehicle ("Mecanum crank") to enhance its rough-terrain locomotion. Compared with other holonomic vehicles designed for rough terrain locomotion, the proposed design exhibited superior capability in the longitudinal wheel direction. To avoid the conflict between the crank leg and Mecanum wheel, this paper proposes the use of a differential gear system. The simple structure of the crank leg enables the implementation of a swing equalizer and roller grousers, which further enhance its rough-terrain locomotion. In longitudinal locomotion experiments, the proposed mechanism could climb a 95 mm-high step, which is 95% of the Mecanum wheel diameter. Furthermore, Mecanum crank could climb miniature scale stairs and execute lateral locomotion on them.
Parallel Actuation of Nanorod Swarm and Nanoparticle Swarm to Different Targets

After years of development, various swarms of robots have been proposed for many complicated tasks, such as forming patterns, cooperative locomotion, and adapting to different environments. However, controlling microrobotic swarms is still a challenging task owing to the lacking of integrated devices on the small-scale agents, and actuation of multiple microrobotic swarms to different targets under the same global input will be even more difficult. In this work, we present a swarm of nickel nanorods and its diverse locomotion velocity compared with Fe3O4 nanoparticle swarms is implemented for actuating the two swarms to different targets under the same customized oscillating magnetic field. The effects of the magnetic anisotropy of agents on the macroscopic swarm behaviour are analysed theoretically. To prove the strategy, the speeds of the two swarms were characterized through experiments, and demonstrations were conducted to show the capability of driving the two swarms to different locations in the same environment. Furthermore, parallel locomotion of the two swarms towards opposite directions was also achieved on a tilted substrate. This work has proved the feasibility of simultaneously actuating two swarms to diverse targets and promoted fundamental understandings of microrobotic swarms.
Point Set Registration With Semantic Region Association Using Cascaded Expectation Maximization

We introduce a new solution to point set registration, a fundamental geometric problem occurring in many computer vision and robotics applications. We consider the specific case in which the point sets are segmented into semantically annotated parts. Such information may for example come from object detection or instance-level semantic segmentation in a registered RGB image. Existing methods incorporate the additional information to restrict or re-weight the point-pair associations occurring throughout the registration process. We introduce a novel hierarchical association framework for a simultaneous inference of semantic region association likelihoods. The formulation is elegantly solved using cascaded expectation-maximization. We conclude by demonstrating a substantial improvement over existing alternatives on open RGBD datasets.
Rapid Solution of Cosserat Rod Equations via a Nonlinear Partial Observer

The Cosserat rod equations are used to model continuum and soft robots. Solving these equations are computationally expensive, particularly due to mixed boundary values and kinematic constraints. In this paper, we present a novel nonlinear observer that can rapidly estimate the solution of the Cosserat rod equations. We present details of the observer design and analyse its convergence and stability. Furthermore, we compare the accuracy and performance of the observer with common solvers used in the literature. Our results show that the proposed observer can significantly improve the computational efficiency of continuum robots’ models and estimates the solution of the Cosserat rod equations 7 times faster than common solvers.
Online DCM Trajectory Adaptation for Push and Stumble Recovery during Humanoid Locomotion

In this paper, we present a highly efficient Divergent Component of Motion (DCM) reference trajectory generator capable of adapting online to large perturbations acting on the center-of-mass (push recovery) and on the swing foot (stumble recovery). For push recovery, we propose an analytic solution for a footstep adjustment strategy based on the DCM dynamics. The proposed algorithm considers double support phases explicitly and is active throughout the motion, i.e., during both single and double support phases. For stumble recovery, we introduce a continuous DCM trajectory adaptation based on the instantaneous tracking error of the swing foot. Our method is highly efficient, computing a push recovery solution within 10 microseconds on the robot hardware. Furthermore, it achieves robust locomotion for large external perturbations, which we demonstrate in simulations and experiments with the humanoid robot TORO.
Uncertainty-Aware Fast Curb Detection Using Convolutional Networks in Point Clouds

Curb detection is an essential function of autonomous vehicles in urban areas. However, curbs are difficult to detect in complex urban environments in which many dynamic objects exist. Additionally, curbs appear in a variety of shapes and sizes. Previous studies have been based on the traditional pipeline, which consists of the extraction and aggregation of hand-crafted features that are then fed to classifiers. However, this sequential process is inefficient and designing the hand-crafted features is a complex process. Recently, this kind of process has been replaced by Deep Neural Networks (DNN), in which classifiers and features are learned from large-scale data. Very few works have exploited DNN for the curb detection problem. Most works use multi-modal sensor-based methods that combine images and accumulated 3D point clouds from LIDAR. However, these approaches require synchronization and calibration between sensors. In addition, they do not quantify the uncertainty of their predictions for autonomous system safety. In this paper, we present a two-stage DNN-based curb detection method that includes uncertainty quantification. An autoencoder-based network predicts the curbs, and then conditional neural processes rectify the predictions with uncertainty estimations. The experimental results show that our approach achieves high accuracy and recall in complex areas. We also constructed a large-scale dataset to create benchmarks consisting of approximately 5,224 scans with bird’s-eye view labels collected from urban areas. To the best of our knowledge, there are no public datasets for DNN-based curb detectors. The benchmarks and datasets are publicly available at https://github.com/YounghwaJung/curb_detection_DNN.
Automated design of underactuated monolithic soft robotics structures with multiple predefined end poses

The increasing frequency of robot interactions with humans and complex environments motivates the design and control of soft robotic systems made of compliant structures. Additive manufacturing processes enable the creation of new robotic systems and mechanisms that gain their compliance extrinsically through their design, rather than simply their material. With the ability to rapidly and affordably manufacture such mechanisms, the interest in producing custom task-specific underactuated mechanisms has grown. These mechanisms may be useful for highly repetitive tasks or complex situations where the simplicity of control outweighs the versatility of a more traditional robotic system. In the course of this work, new underactuated monolithic soft robotic mechanisms are presented along with automated design processes. The structures can reach multiple arbitrary end effector poses and are controlled with as few actuators as possible. Two methods for the automated design of such structures via inverse kinematics and two concepts for the mechanical realization are presented. Several functional models were created to illustrate possible application scenarios and validate the mechanical concepts. A novel contact surface was also developed and shown to improve the accuracy performance of these structures. These automated design methods contribute toward the general accessibility and usability of multi-pose underactuated extrinsically soft mechanisms.
RIL: Riemannian Incremental Learning of the Inertial Properties of the Robot Body Schema

We transform classical robot inertial parameter identification into an online learning problem by integrating state-of-the-art gradient descent techniques and first-order principles from mechanics and differential geometry. Through this, incremental learning of fully physically feasible inertial properties without requiring any prior information is made possible. This is achieved using a version of Riemannian gradient descent equipped with experience replay that guarantees feasible parameter updates at all times during learning. Analysis of the method's performance are done on a virtual manipulator focusing on the influence that different measurement setups have on the estimation as well as on parameter feasibility and re-learning. Finally, we present experimental results on a real 7 DoF manipulator and evaluate the quality of the generated inverse dynamics torques and the corresponding model error.
Automatic Clustering of DNA Sequences With Intelligent Techniques

With the discovery of new DNAs, a fundamental problem arising is how to categorize those DNA sequences into correct species. Unfortunately, identifying all data groups correctly and assigning a set of DNAs into k clusters where k must be predefined are one of the major drawbacks in clustering analysis, especially when the data have many dimensions and the number of clusters is too large and hard to guess. Furthermore, finding a similarity measure that preserves the functionality and represents both the composition and distribution of the bases in a DNA sequence is one of the main challenges in computational biology. In this paper, a new soft computing metaheuristic framework is introduced for automatic clustering to generate the optimal cluster formation and to determine the best estimate for the number of clusters. Pulse coupled neural network (PCNN) is utilized for the calculation of DNA sequence similarity or dissimilarity. Bat algorithm is hybridized with the well-known genetic algorithm to solve the automatic data clustering problem. Extensive computational experiments are conducted on the expanded human oral microbiome database (eHOMD). A comparative study between the experimental results shows that the proposed hybrid algorithm achieved superior performance over the standard genetic algorithm and bat algorithm. Moreover, the hybrid performance was compared with competing algorithms from the literature review to ascertain its superiority. Mann-Whitney-Wilcoxon rank-sum test is conducted to statistically validate the obtained clusters.
The Effect of Stray Capacitance to the Common Mode Current on Three-Phase System

The common-mode (CM) current phenomena is one of many problems in the EMC world due to the radiated magnetic field caused by it. A power transmission line with a delta-connection both generator and load normally do not have a connection to ground to establish a line for the return current. To determine the CM current, finding the stray capacitances to the ground is highly important because they are used as the return path for the CM current. In this paper, the investigation of predicting the CM current flowing through the stray capacitances will be done at a three-phase equivalent system of a wind turbine (WT). The wind turbine body is the place where the CM current flows, due to the stray capacitances between the power cables and the WT body around it. The CM current can be determined using the current magnitudes in a pointer-image method, which has a good agreement for CM current prediction and it might become a very useful tool applicable to measurements.
A Geometry Feature Aggregation Method for Point Cloud Classification and Segmentation

In spite of the good performance of convolutional neural network (CNN) and graph neural network (GNN) in 3D point cloud classification and segmentation at present, to aggregate local information of point clouds and improve the robustness of geometric transformation are still challenging problems. In order to tackle the problems, we propose Geometry Feature Aggregation Network (GFA-Net), which can effectively learn the context information of each point to aggregate local information, so as to enhance the robustness of rotation and translation. Compared with the current popular method GNN that convolves on nearby points in Euclidean space, GFA-Net can better aggregate the geometric features around the points. GFA-Net uses the Laplacian feature mapping to reduce dimensions, and aggregates the nearest neighbor features in the space after dimensionality reduction, and fuses them with the nearest neighbor features of Euclidean space, so as to better obtain the geometric features of each point. Then, points are grouped with geometric features, so that nearby points are insensitive to geometric transformations such as rotation and translation. This method allows GFA-Net to better obtain holistic geometry features, such as symmetry. In addition, we use attention mechanism instead of pooling, so that important neighborhood information can be learned automatically and information loss can be reduced. We conduct extensive experiments on public datasets ModelNet40 and ShapeNet Part. The experimental results show that GFA-Net achieves very good performance, which is very close to the current state-of-the-art methods, and GFA-Net has better robustness.
Optimal Scheduling Framework of Electricity-Gas-Heat Integrated Energy System Based on Asynchronous Advantage Actor-Critic Algorithm

The optimal scheduling of integrated energy system (IES) can improve energy efficiency and economic operation. However, the existing scheduling methods cannot accurately handle the dynamic changes of supply and demand sides in the electricity-gas-heat IES thanks to their power uncertainties. To tackle this problem, an optimal dispatch framework based on the asynchronous advantage actor-critic (A3C) method of IES is proposed. Firstly, we describe the dispatch problem of IES with multiple uncertainties as Markov decision process (MDP) according to the corresponding mathematical models and constraints. Then, the dispatch framework based on A3C is developed to optimize the control decision for supply and demand sides by asynchronous learning of agents and to reduce the relevance of parameters update of neural networks by the multi-agent utilization of the central processing unit (CPU) multi-threading function. Finally, our proposed methods are verified by the simulations. Compared with the previous optimization algorithms, the training time of the proposed methods is shortened by 37% and 30%, and the daily average operating cost is reduced by 3%, 5.2% and 8.7%.
Oscillations at 300-400 GHz in Structure-Simplified Resonant-Tunneling-Diode Oscillators with Rectangular-Cavity Resonators

We demonstrated oscillations of resonant-tunneling-diode (RTD) terahertz oscillators with rectangular-cavity resonators operating at 300-400 GHz range. We proposed a simplified oscillator structure to reduce fabrication process and a multi-layer resist process to form the cavity resonator. The oscillation with large RTD mesa areas was obtained, which indicates the possibility of high output power, although the structure has not yet been optimized in terms of the antenna length and RTD current density. The experimental results agreed with theoretical calculations, and high output power is expected by the structure optimization.
Zero-shot Policy Learning with Spatial Temporal Reward Decomposition on Contingency-aware Observation

It is a long-standing challenge to enable an intelligent agent to learn in one environment and generalize to an unseen environment without further data collection and finetuning. In this paper, we consider a zero shot generalization problem setup that complies with biological intelligent agents’ learning and generalization processes. The agent is first presented with previous experiences in the training environment, along with task description in the form of trajectory-level sparse rewards. Later when it is placed in the new testing environment, it is asked to perform the task without any interaction with the testing environment. We find this setting natural for biological creatures and at the same time, challenging for previous methods. Behavior cloning, state-of-art RL along with other zero-shot learning methods perform poorly on this benchmark. Given a set of experiences in the training environment, our method learns a neural function that decomposes the sparse reward into particular regions in a contingency-aware observation as a per step reward. Based on such decomposed rewards, we further learn a dynamics model and use Model Predictive Control (MPC) to obtain a policy. Since the rewards are decomposed to finer-granularity observations, they are naturally generalizable to new environments that are composed of similar basic elements. We demonstrate our method on a wide range of environments, including a classic video game – Super Mario Bros, as well as a robotic continuous control task. Please refer to the project page for more visualized results.1
Deep3DRanker: A Novel Framework for Learning to Rank 3D Models with Self-Attention in Robotic Vision

Research on generating or processing point clouds has become an increasingly popular domain in robotic research due to its extensive applications, such as robotic grasping, augmented reality and autonomous vehicle navigation. In this paper, we explore a new research area on point clouds - Learning to rank 3D models captured from a single depth image. In the Learning To Rank (LTR) task, we aim at optimizing the order of a list of 3D models according to the given query. Inspired by the recent advances in Natural Language Processing (NLP), we propose a novel framework, namely Deep3DRanker, for ranking 3D models by leveraging graph-based encoding and self-attention mechanisms. Comprehensive experiments are conducted to validate our methods on publicly available YCB synthetic and YCB video datasets. The promising results have shown that our proposed framework is generic enough to be applicable with any combinations of randomly positioned, oriented, and unseen object items with accuracy ranging from 59.2% to 94.9%, which shows great potentials of the proposed framework for robotic applications, in particular, for making decisions under different circumstances.
Evolvable Motion-planning Method using Deep Reinforcement Learning

A motion-planning method that can adapt to changes in the surrounding environment is proposed and evaluated. Automation of work is progressing in factories and distribution warehouses due to labor shortages. However, utilizing robots for transport operations in a distribution warehouse faces a problem; that is, tasks for setting up a robot, such as adjustment of acceleration for stabilization of the transportation operation, are time consuming. To solve that problem, we developed an "evolvable robot motion-planning method. " The aim of this method is to reduce the preparation cost by allowing the robot to automatically learn the optimized acceleration according to the weight and center of gravity of the objects to be transported. It was experimentally demonstrated that the proposed method can learn the optimized acceleration control from time-series data such as sensor information. The proposed method was evaluated in a simulator environment, and the results of the evaluation demonstrate that the learned model reduced the inertial force due to the acceleration of robot motion and shortened the transport time by 35% compared with the conventional method of manual adjustment. The proposed method was also evaluated in a real machine environment, and the evaluation results demonstrate that the method can be applied to a real robot. Since the speed of the robot does not need to be adjusted in the case of the proposed method, the adjustment man-hours can be reduced.
Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

In this paper, we present a novel data-driven design method for the human-robot interaction (HRI) system, where a given task is achieved by cooperation between the human and the robot. The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design. The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop, while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop. Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters. In the inner-loop, a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement. On this basis, an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space. The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.