Reviewerly/exHarmony huggingface dataset:
The extended codes of a family of reversible MDS cyclic codes

A linear code with parameters [ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> +1] is called a maximum distance separable (MDS for short) code. A linear code with parameters [ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ] is said to be almost maximum distance separable (AMDS for short). A linear code is said to be near maximum distance separable (NMDS for short) if both the code and its dual are AMDS. MDS codes are very important in both theory and practice. There is a classical construction of a [ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> +1,2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</i> −1, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> −2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</i> +3] MDS code for each <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</i> with 1 ≤ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</i> ≤ ⌊ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> +1/2⌋, which is a reversible and cyclic code. The objective of this paper is to study the extended codes of this family of MDS codes. Two families of MDS codes and several families of NMDS codes are obtained. The NMDS codes have applications in finite geometry, cryptography and distributed and cloud data storage systems. The weight distributions of some of the extended codes are determined.
GP-Guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments

Robotic navigation in unknown, cluttered environ-ments with limited sensing capabilities poses significant chal-lenges in robotics. Local trajectory optimization methods, such as Model Predictive Path Intergal (MPPI), are a promising solution to this challenge. However, global guidance is required to ensure effective navigation, especially when encountering challenging environmental conditions or navigating beyond the planning horizon. This study presents the GP-MPPI, an online learning-based control strategy that integrates MPPI with a local perception model based on Sparse Gaussian Process (SGP). The key idea is to leverage the learning capability of SGP to construct a variance (uncertainty) surface, which enables the robot to learn about the navigable space surrounding it, identify a set of suggested subgoals, and ultimately recommend the optimal subgoal that minimizes a predefined cost function to the local MPPI planner. Afterward, MPPI computes the optimal control sequence that satisfies the robot and collision avoidance constraints. Such an approach eliminates the necessity of a global map of the environment or an offline training process. We validate the efficiency and robustness of our proposed control strategy through both simulated and real-world experiments of 2D autonomous navigation tasks in complex unknown en-vironments, demonstrating its superiority in guiding the robot safely towards its desired goal while avoiding obstacles and escaping entrapment in local minima. The GPU implementation of GP-MPPI, including the supplementary video, is available at https://github.com/IhabMohamed/GP-MPPI.
Predicting Center of Mass by Iterative Pushing for Object Transportation and Manipulation

Robotic manipulation tasks rely on a plethora of environmental and payload information. One critical piece of information for accurate manipulation is the center of mass (CoM) of the object, which is essential for estimating the dynamic response of the system and determining the payload placement. Traditionally, the CoM of a payload is provided prior to manipulation. In order to create a more robust and comprehensive system, this information should be collected by the robotic agent before or during the task run time. This paper presents a method for approximating the CoM of a planar object using a small-scale mobile robot to inform manipulation tasks. On average, our system is able to converge on a CoM estimate in under 30 seconds in simulation and 20 seconds in experiment, with a relative error of 4.95% and 5.46%, respectively.
Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment.
Multi-Agent Multi-Objective Ergodic Search Using Branch and Bound

Search and rescue applications often need multiple agents to complete a set of conflicting tasks. This paper studies a Multi-Agent Multi-Objective Ergodic Search (MA-MO-ES) approach to this problem where each objective or task is to cover a domain subject to an information map. The goal is to allocate coverage tasks to agents so that all maps are explored ergodically. The combinatorial nature of task allocation makes it computationally expensive to solve for optimal allocation using brute force. Apart from a large number of possible allocations, computing the cost of a task allocation is itself an expensive planning problem. To mitigate the computational challenge, we present a branch and bound-based algorithm with pruning techniques that reduce the number of allocations to be searched to find optimal coverage task allocation. We also present an approach to leverage the similarity between information maps to further reduce computation. Extensive testing on 147 randomly generated test cases shows an order of magnitude improvement in runtime compared to an exhaustive brute force approach.
The Effects of Robot Motion on Comfort Dynamics of Novice Users in Close-Proximity Human-Robot Interaction

Effective and fluent close-proximity human-robot interaction requires understanding how humans get habituated to robots and how robot motion affects human comfort. While prior work has identified humans' preferences over robot motion characteristics and studied their influence on comfort, we are yet to understand how novice first-time robot users get habituated to robots and how robot motion impacts the dynamics of comfort over repeated interactions. To take the first step towards such understanding, we carry out a user study to investigate the connections between robot motion and user comfort and habituation. Specifically, we study the influence of workspace overlap, end-effector speed, and robot motion legibility on overall comfort and its evolution over repeated interactions. Our analyses reveal that workspace overlap, in contrast to speed and legibility, has a significant impact on users' perceived comfort and habituation. In particular, lower workspace overlap leads to users reporting significantly higher overall comfort, lower variations in comfort, and fewer fluctuations in comfort levels during habituation.
Inertial Propulsion Robot Using the Shape Characteristics of a Streamlined Body Frame

We have been investigating a crawling-like loco-motion robot to make it efficiently slide forward based on a simple system and control mechanisms on a slippery level surface, where the motion of the center of mass plays an important role. In this paper, we induce an effective motion of the center of mass considering a streamlined body shape of a locomotion robot in which a pendulum is installed. First, we derive the equation of motion and a control input to achieve a desired motion of the inner pendulum. Second, we formulate constraint conditions between the streamlined body and a slippery floor. Third, we demonstrate the numerical simulation, and the robot steadily slides forward by adopting the streamlined shape as the body frame. Fourth, we verify the numerical results through experiments, and the experimental results exhibit a similar tendency compared with the numerical results. Fifth, we find a local minimum value of locomotion efficiency based on Bayesian optimization which is a class of machine-learning-based optimization, and we achieve exceedingly efficient locomotion of the robot on the slippery floor at the local minimum in both the simulation and experiment.
Image Restoration via UAVFormer for Under-Display Camera of UAV

The exposed cameras of UAVs can shake, shift, or even malfunction under the influence of harsh weather, while the add-on devices (Dupont lines) are very vulnerable to dam-age. Although we can place a low-cost transparent film overlay around the camera to protect it, this would also introduce image degradation issues (such as oversaturation, astigmatism, etc). To tackle the image degradation problem caused by overlaying transparent film, in this paper we propose a novel method to enhance the visual experience by adapting a deep network with UAV characteristics. Specifically, we propose a customized Transformer named UAVFormer to recover the image, which has a key module at each stage based on the Swin Transformer with local awareness (LAT). In the end, we use an evidential fusion algorithm to integrate the generated images at each stage to obtain a high-quality result. Furthermore, we create a high-resolution under-display camera dataset to support the training and testing of compared models. Our model can conduct high-quality recovery of images of 2K resolution on some embedded devices (Raspberry Pi 4b) in realtime. The URL for the code at https://github.com/zzr-idam/UAVFormer.
Robo-Centric ESDF: A Fast and Accurate Whole-Body Collision Evaluation Tool for Any-Shape Robotic Planning

For letting mobile robots travel flexibly through complicated environments, increasing attention has been paid to the whole-body collision evaluation. Most existing works either opt for the conservative corridor-based methods that impose strict requirements on the corridor generation, or ESDF-based methods that suffer from high computational overhead. It is still a great challenge to achieve fast and accurate whole-body collision evaluation. In this paper, we propose a Robo-centric ESDF (RC-ESDF) that is pre-built in the robot body frame and is capable of seamlessly applied to any-shape mobile robots, even for those with non-convex shapes. RC-ESDF enjoys lazy collision evaluation, which retains only the minimum information sufficient for whole-body safety constraint and significantly speeds up trajectory optimization. Based on the analytical gradients provided by RC-ESDF, we optimize the position and rotation of robot jointly, with whole-body safety, smoothness, and dynamical feasibility taken into account. Extensive simulation and real-world experiments verified the reliability and generalizability of our method.
Design Space Exploration on Efficient and Accurate Human Pose Estimation from Sparse IMU-Sensing

Human Pose Estimation (HPE) to assess human motion in sports, rehabilitation or work safety requires accurate sensing without compromising the sensitive underlying personal data. Therefore, local processing is necessary and the limited energy budget in such systems can be addressed by Inertial Measurement Units (IMU) instead of common camera sensing. The central trade-off between accuracy and efficient use of hardware resources is rarely discussed in research. We address this trade-off by a simulative Design Space Exploration (DSE) of a varying quantity and positioning of IMU -sensors. First, we generate IMU-data from a publicly available body model dataset for different sensor configurations and train a deep learning model with this data. Additionally, we propose a combined metric to assess the accuracy-resource trade-off. We used the DSE as a tool to evaluate sensor configurations and identify beneficial ones for a specific use case. Exemplary, for a system with equal importance of accuracy and resources, we identify an optimal sensor configuration of 4 sensors with a mesh error of 6.03 cm, increasing the accuracy by 32.7 % and reducing the hardware effort by two sensors compared to state of the art. Our work can be used to design health applications with well-suited sensor positioning and attention to data privacy and resource-awareness.
Domain Randomization for Robust, Affordable and Effective Closed-Loop Control of Soft Robots

Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability. However, the potentially infinite number of Degrees of Freedom makes their modeling a daunting task, and in many cases only an approximated description is available. This challenge makes reinforcement learning (RL) based approaches inefficient when deployed on a realistic scenario, due to the large domain gap between models and the real platform. In this work, we demonstrate, for the first time, how Domain Randomization (DR) can solve this problem by enhancing RL policies for soft robots with: i) robustness w.r.t. unknown dynamics parameters; ii) reduced training times by exploiting drastically simpler dynamic models for learning; iii) better environment exploration, which can lead to exploitation of environmental constraints for optimal performance. Moreover, we introduce a novel algorithmic extension to previous adaptive domain randomization methods for the automatic inference of dynamics parameters for deformable objects. We provide an extensive evaluation in simulation on four different tasks and two soft robot designs, opening interesting perspectives for future research on Reinforcement Learning for closed-loop soft robot control.
Efficient Path Planning In Manipulation Planning Problems by Actively Reusing Validation Effort

The path planning problems arising in manipulation planning and in task and motion planning settings are typically repetitive: the same manipulator moves in a space that only changes slightly. Despite this potential for reuse of information, few planners fully exploit the available information. To better enable this reuse, we decompose the collision checking into reusable, and non-reusable parts. We then treat the sequences of path planning problems in manipulation planning as a multiquery path planning problem. This allows the usage of planners that actively minimize planning effort over multiple queries, and by doing so, actively reuse previous knowledge. We implement this approach in EIRM* and effort ordered LazyPRM*, and benchmark it on multiple simulated robotic examples. Further, we show that the approach of decomposing collision checks additionally enables the reuse of the gained knowledge over multiple different instances of the same problem, i.e., in a multiquery manipulation planning scenario. The planners using the decomposed collision checking out-perform the other planners in initial solution time by up to a factor of two while providing a similar solution quality.
High resolution brain PET image reconstruction with deep progressive learning

Positron emission tomography (PET) can be used for the diagnosis and therapeutic monitoring of brain tumors and neurodegenerative diseases. However, brain PET images typically require high resolution and high image quality to obtain sufficient diagnostic value due to the complex and delicate anatomical structure of the brain. It further limits the use of low-dose and fast acquisition schemes on conventional PET systems. In this work, we proposed a deep progressive learning based super-resolution reconstruction (DPR) method for brain PET imaging. Specifically, a denoising network and an enhanced super-resolution network were designed and used in the reconstruction. The algorithm was evaluated using an XCAT digital brain phantom based on GATE simulation, and compared to OSEM with different pixel sizes. The evaluation results indicate that DPR achieves the best performance in metrics as SSMI, PSNR, Contrast, and background COV, especially under low count conditions. In conclusion, the proposed DPR algorithm makes it possible to obtain high-resolution and high-quality brain PET images under low-dose and/or fast scanning conditions.
Upper Bounds for Localization Errors in 2D Human Pose Estimation

Obtaining reliable detections of a human is crucial for many safety-related robotic tasks. This can be done by human pose estimation methods, which predict the position of several different keypoints of the human body. In most cases, recent approaches based on neural networks produce ‘good’ results, i.e. predictions with small localization errors, however, large errors do also occur. For an individual keypoint prediction, the magnitude of the error is unknown, posing a risk to safety. In this work, we extend a neural network architecture for single-person 2D human pose estimation, so that it predicts not only the keypoints of the human body, but also corresponding upper bounds for their localization errors. These upper bounds correspond to the neural network's confidence in its output, and are obtained by one of two general strategies based on (i) a direct estimation of the localization error or (ii) the predicted standard deviations of a 2D Gaussian. We propose several approaches employing these strategies and evaluate them on the MPII Human Pose dataset. In addition, we consider two quality criteria for the results: closeness of the predicted keypoint position to the actual one, and closeness of the predicted upper bound to the localization error. The best results are achieved by a Gaussian-based approach, which predicted correct upper bounds in 94.7% of the cases, while also sufficiently fulfilling the quality criteria.
A Game-Theoretic Framework for Joint Forecasting and Planning

Planning safe robot motions in the presence of humans requires reliable forecasts of future human motion. However, simply predicting the most likely motion from prior interactions does not guarantee safety. Such forecasts fail to model the long tail of possible events, which are rarely observed in limited datasets. On the other hand, planning for worst-case motions leads to overtly conservative behavior and a “frozen robot”. Instead, we aim to learn forecasts that predict counterfactuals that humans guard against. We propose a novel game-theoretic framework for joint planning and forecasting with the payoff being the performance of the planner against the demonstrator, and present practical algorithms to train models in an end-to-end fashion. We demonstrate that our proposed algorithm results in safer plans in a crowd navigation simulator and real-world datasets of pedestrian motion. We release our code at https://github.com/portal-cornell/Game-Theoretic-Forecasting-Planning.
Data Structure Sparse Table

This article discusses the sparse table data structure: description, capabilities, and scope of application. The results of comparison with such data structures as a segment tree and a sqrt decomposition for two problems are shown. Sparse Table is a data structure, that allows answering static range queries. It can answer most range queries in O(log n), but it efficiently answers range minimum queries (or equivalent range maximum queries) in O(1) time. It does preprocessing beforehand so that the queries can be answered efficiently. The origin of the data structure is related to the graph term lowest common ancestor. In graph theory and computer science, the lowest common ancestor (also called least common ancestor) of two nodes v and w in a tree or directed acyclic graph T is the lowest (i.e. deepest) node that has both v and w as descendants, where we define each node to be a descendant of itself (so if v has a direct connection from w, w is the lowest common ancestor). We can solve the problem of finding the least common ancestor using a sparse table. The listing codes are presented in the C++ programming language.
Bidirectional Search Strategy for Incremental Search-based Path Planning

Planning a collision-free path efficiently among obstacles is crucial in robotics. Conventional one-shot unidirectional path planning algorithms work well in the static environment, but cannot respond to the environment changes timely in the dynamic environment. To tackle this issue and improve the search efficiency, we propose a bidirectional incremental search method, Bidirectional Lifelong Planning A* (BLPA*), which searches in the forward and backward directions and performs incremental search bidirectionally when the environment changes. Furthermore, inspired by the robot perception range limitation and BLPA*, we propose the fractional bidirectional D* Lite (fBD* Lite(d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> )), which constraints the forward search to the robot perception range and uses the backward search to expand the rest area. Our simulation results demonstrate BLPA* and mD* Lite(d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> ) can achieve superior performance in the dynamic environment. It reveals that the bidirectional incremental search strategy can be a general and efficient technique for graph-search-based robot path planning methods.
Prototypical Contrastive Transfer Learning for Multimodal Language Understanding

Although domestic service robots are expected to assist individuals who require support, they cannot currently interact smoothly with people through natural language. For example, given the instruction “Bring me a bottle from the kitchen,” it is difficult for such robots to specify the bottle in an indoor environment. Most conventional models have been trained on real-world datasets that are labor-intensive to collect, and they have not fully leveraged simulation data through a transfer learning framework. In this study, we propose a novel transfer learning approach for multimodal language understanding called Prototypical Contrastive Transfer Learning (PCTL), which uses a new contrastive loss called Dual ProtoNCE. We introduce PCTL to the task of identifying target objects in domestic environments according to free-form natural language instructions. To validate PCTL, we built new real-world and simulation datasets. Our experiment demonstrated that PCTL outperformed existing methods. Specifically, PCTL achieved an accuracy of 78.1 %, whereas simple fine-tuning achieved an accuracy of 73.4 %.