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Spatio-Temporal Motion Retargeting for Quadruped Robots arXiv.cs.RO Pub Date : 2024-04-17 Taerim Yoon, Dongho Kang, Seungmin Kim, Minsung Ahn, Stelian Coros, Sungjoon Choi
This work introduces a motion retargeting approach for legged robots, which aims to create motion controllers that imitate the fine behavior of animals. Our approach, namely spatio-temporal motion retargeting (STMR), guides imitation learning procedures by transferring motion from source to target, effectively bridging the morphological disparities by ensuring the feasibility of imitation on the target
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Following the Human Thread in Social Navigation arXiv.cs.RO Pub Date : 2024-04-17 Luca Scofano, Alessio Sampieri, Tommaso Campari, Valentino Sacco, Indro Spinelli, Lamberto Ballan, Fabio Galasso
The success of collaboration between humans and robots in shared environments relies on the robot's real-time adaptation to human motion. Specifically, in Social Navigation, the agent should be close enough to assist but ready to back up to let the human move freely, avoiding collisions. Human trajectories emerge as crucial cues in Social Navigation, but they are partially observable from the robot's
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Saturated RISE control for considering rotor thrust saturation of fully actuated multirotor arXiv.cs.RO Pub Date : 2024-04-17 Dongjae Lee, H. Jin Kim
This work proposes a saturated robust controller for a fully actuated multirotor that takes disturbance rejection and rotor thrust saturation into account. A disturbance rejection controller is required to prevent performance degradation in the presence of parametric uncertainty and external disturbance. Furthermore, rotor saturation should be properly addressed in a controller to avoid performance
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Autonomous aerial perching and unperching using omnidirectional tiltrotor and switching controller arXiv.cs.RO Pub Date : 2024-04-17 Dongjae Lee, Sunwoo Hwang, Jeonghyun Byun, Seung Jae Lee, H. Jin Kim
Aerial unperching of multirotors has received little attention as opposed to perching that has been investigated to elongate operation time. This study presents a new aerial robot capable of both perching and unperching autonomously on/from a ferromagnetic surface during flight, and a switching controller to avoid rotor saturation and mitigate overshoot during transition between free-flight and perching
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Milling using two mechatronically coupled robots arXiv.cs.RO Pub Date : 2024-04-17 Max Goebels, Jan Baumgärtner, Tobias Fuchs, Edgar Mühlbeier, Alexander Puchta, Jürgen Fleischer
Industrial robots are commonly used in various industries due to their flexibility. However, their adoption for machining tasks is minimal because of the low dynamic stiffness characteristic of serial kinematic chains. To overcome this problem, we propose coupling two industrial robots at the flanges to form a parallel kinematic machining system. Although parallel kinematic chains are inherently stiffer
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Towards Human Awareness in Robot Task Planning with Large Language Models arXiv.cs.RO Pub Date : 2024-04-17 Yuchen Liu, Luigi Palmieri, Sebastian Koch, Ilche Georgievski, Marco Aiello
The recent breakthroughs in the research on Large Language Models (LLMs) have triggered a transformation across several research domains. Notably, the integration of LLMs has greatly enhanced performance in robot Task And Motion Planning (TAMP). However, previous approaches often neglect the consideration of dynamic environments, i.e., the presence of dynamic objects such as humans. In this paper,
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Learning Social Navigation from Demonstrations with Deep Neural Networks arXiv.cs.RO Pub Date : 2024-04-17 Yigit Yildirim, Emre Ugur
Traditional path-planning techniques treat humans as obstacles. This has changed since robots started to enter human environments. On modern robots, social navigation has become an important aspect of navigation systems. To use learning-based techniques to achieve social navigation, a powerful framework that is capable of representing complex functions with as few data as possible is required. In this
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Accuracy and repeatability of a parallel robot for personalised minimally invasive surgery arXiv.cs.RO Pub Date : 2024-04-17 Doina PislaLS2N - équipe RoMas, LS2N, Paul TucanLS2N - équipe RoMas, LS2N, Damien ChablatLS2N - équipe RoMas, LS2N, Nadim Al HajjarUMP, Andra CiocanUMP, Adrian PislaUMP, Alexandru PuscaUMP, Corina RaduUMP, Grigore Pop, Bogdan Gherman
The paper presents the methodology used for accuracy and repeatability measurements of the experimental model of a parallel robot developed for surgical applications. The experimental setup uses a motion tracking system (for accuracy) and a high precision measuring arm for position (for repeatability). The accuracy was obtained by comparing the trajectory data from the experimental measurement with
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OVAL-Prompt: Open-Vocabulary Affordance Localization for Robot Manipulation through LLM Affordance-Grounding arXiv.cs.RO Pub Date : 2024-04-17 Edmond Tong, Anthony Opipari, Stanley Lewis, Zhen Zeng, Odest Chadwicke Jenkins
In order for robots to interact with objects effectively, they must understand the form and function of each object they encounter. Essentially, robots need to understand which actions each object affords, and where those affordances can be acted on. Robots are ultimately expected to operate in unstructured human environments, where the set of objects and affordances is not known to the robot before
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Machine-Learning-Enhanced Soft Robotic System Inspired by Rectal Functions for Investigating Fecal incontinence arXiv.cs.RO Pub Date : 2024-04-17 Zebing Mao, Sota Suzuki, Hiroyuki Nabae, Shoko Miyagawa, Koichi Suzumori, Shingo Maeda
Fecal incontinence, arising from a myriad of pathogenic mechanisms, has attracted considerable global attention. Despite its significance, the replication of the defecatory system for studying fecal incontinence mechanisms remains limited largely due to social stigma and taboos. Inspired by the rectum's functionalities, we have developed a soft robotic system, encompassing a power supply, pressure
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Safety-critical Autonomous Inspection of Distillation Columns using Quadrupedal Robots Equipped with Roller Arms arXiv.cs.RO Pub Date : 2024-04-16 Jaemin Lee, Jeeseop Kim, Aaron D. Ames
This paper proposes a comprehensive framework designed for the autonomous inspection of complex environments, with a specific focus on multi-tiered settings such as distillation column trays. Leveraging quadruped robots equipped with roller arms, and through the use of onboard perception, we integrate essential motion components including: locomotion, safe and dynamic transitions between trays, and
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FlexMap Fusion: Georeferencing and Automated Conflation of HD~Maps with OpenStreetMap arXiv.cs.RO Pub Date : 2024-04-16 Maximilian Leitenstern, Florian Sauerbeck, Dominik Kulmer, Johannes Betz
Today's software stacks for autonomous vehicles rely on HD maps to enable sufficient localization, accurate path planning, and reliable motion prediction. Recent developments have resulted in pipelines for the automated generation of HD maps to reduce manual efforts for creating and updating these HD maps. We present FlexMap Fusion, a methodology to automatically update and enhance existing HD vector
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End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving arXiv.cs.RO Pub Date : 2024-04-16 Satya R. Jaladi, Zhimin Chen, Narahari R. Malayanur, Raja M. Macherla, Bing Li
The current autonomous stack is well modularized and consists of perception, decision making and control in a handcrafted framework. With the advances in artificial intelligence (AI) and computing resources, researchers have been pushing the development of end-to-end AI for autonomous driving, at least in problems of small searching space such as in highway scenarios, and more and more photorealistic
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Industrial Cabling in Constrained Environments: a Practical Approach and Current Challenges arXiv.cs.RO Pub Date : 2024-04-16 Tanureza Jaya, Benjamin Michalak, Marcel Radke, Kevin Haninger
Cabling tasks (pulling, clipping, and plug insertion) are today mostly manual work, limiting the cost-effectiveness of electrification. Feasibility for the robotic grasping and insertion of plugs, as well as the manipulation of cables, have been shown in research settings. However, in many industrial tasks the complete process from picking, insertion, routing, and validation must be solved with one
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VG4D: Vision-Language Model Goes 4D Video Recognition arXiv.cs.RO Pub Date : 2024-04-17 Zhichao Deng, Xiangtai Li, Xia Li, Yunhai Tong, Shen Zhao, Mengyuan Liu
Understanding the real world through point cloud video is a crucial aspect of robotics and autonomous driving systems. However, prevailing methods for 4D point cloud recognition have limitations due to sensor resolution, which leads to a lack of detailed information. Recent advances have shown that Vision-Language Models (VLM) pre-trained on web-scale text-image datasets can learn fine-grained visual
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VBR: A Vision Benchmark in Rome arXiv.cs.RO Pub Date : 2024-04-17 Leonardo Brizi, Emanuele Giacomini, Luca Di Giammarino, Simone Ferrari, Omar Salem, Lorenzo De Rebotti, Giorgio Grisetti
This paper presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns
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KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections arXiv.cs.RO Pub Date : 2024-04-17 Chuheng Wei, Guoyuan Wu, Matthew J. Barth, Amr Abdelraouf, Rohit Gupta, Kyungtae Han
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems. However, it presents unique challenges, due to the complex roadway layout at intersections, involvement of traffic signal controls, and interactions among different types of road users. To address these issues, we present in this paper a novel model called Knowledge-Informed
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TaCOS: Task-Specific Camera Optimization with Simulation arXiv.cs.RO Pub Date : 2024-04-17 Chengyang Yan, Donald Dansereau
The performance of robots in their applications heavily depends on the quality of sensory input. However, designing sensor payloads and their parameters for specific robotic tasks is an expensive process that requires well-established sensor knowledge and extensive experiments with physical hardware. With cameras playing a pivotal role in robotic perception, we introduce a novel end-to-end optimization
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SPONGE: Open-Source Designs of Modular Articulated Soft Robots arXiv.cs.RO Pub Date : 2024-04-16 Tim-Lukas Habich, Jonas Haack, Mehdi Belhadj, Dustin Lehmann, Thomas Seel, Moritz Schappler
Soft-robot designs are manifold, but only a few are publicly available. Often, these are only briefly described in their publications. This complicates reproduction, and hinders the reproducibility and comparability of research results. If the designs were uniform and open source, validating researched methods on real benchmark systems would be possible. To address this, we present two variants of
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SCALE: Self-Correcting Visual Navigation for Mobile Robots via Anti-Novelty Estimation arXiv.cs.RO Pub Date : 2024-04-16 Chang Chen, Yuecheng Liu, Yuzheng Zhuang, Sitong Mao, Kaiwen Xue, Shunbo Zhou
Although visual navigation has been extensively studied using deep reinforcement learning, online learning for real-world robots remains a challenging task. Recent work directly learned from offline dataset to achieve broader generalization in the real-world tasks, which, however, faces the out-of-distribution (OOD) issue and potential robot localization failures in a given map for unseen observation
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Trajectory Planning using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios arXiv.cs.RO Pub Date : 2024-04-16 Levent Ögretmen, Mo Chen, Phillip Pitschi, Boris Lohmann
Conventional trajectory planning approaches for autonomous racing are based on the sequential execution of prediction of the opposing vehicles and subsequent trajectory planning for the ego vehicle. If the opposing vehicles do not react to the ego vehicle, they can be predicted accurately. However, if there is interaction between the vehicles, the prediction loses its validity. For high interaction
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Constrained Object Placement Using Reinforcement Learning arXiv.cs.RO Pub Date : 2024-04-16 Benedikt Kreis, Nils Dengler, Jorge de Heuvel, Rohit Menon, Hamsa Datta Perur, Maren Bennewitz
Close and precise placement of irregularly shaped objects requires a skilled robotic system. Particularly challenging is the manipulation of objects that have sensitive top surfaces and a fixed set of neighbors. To avoid damaging the surface, they have to be grasped from the side, and during placement, their neighbor relations have to be maintained. In this work, we train a reinforcement learning agent
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Learning Deep Dynamical Systems using Stable Neural ODEs arXiv.cs.RO Pub Date : 2024-04-16 Andreas Sochopoulos, Michael Gienger, Sethu Vijayakumar
Learning complex trajectories from demonstrations in robotic tasks has been effectively addressed through the utilization of Dynamical Systems (DS). State-of-the-art DS learning methods ensure stability of the generated trajectories; however, they have three shortcomings: a) the DS is assumed to have a single attractor, which limits the diversity of tasks it can achieve, b) state derivative information
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Learning Symbolic Task Representation from a Human-Led Demonstration: A Memory to Store, Retrieve, Consolidate, and Forget Experiences arXiv.cs.RO Pub Date : 2024-04-16 Luca Buoncompagni, Fulvio Mastrogiovanni
We present a symbolic learning framework inspired by cognitive-like memory functionalities (i.e., storing, retrieving, consolidating and forgetting) to generate task representations to support high-level task planning and knowledge bootstrapping. We address a scenario involving a non-expert human, who performs a single task demonstration, and a robot, which online learns structured knowledge to re-execute
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MPCOM: Robotic Data Gathering with Radio Mapping and Model Predictive Communication arXiv.cs.RO Pub Date : 2024-04-16 Zhiyou Ji, Guoliang Li, Ruihua Han, Shuai Wang, Bing Bai, Wei Xu, Kejiang Ye, Chengzhong Xu
Robotic data gathering (RDG) is an emerging paradigm that navigates a robot to harvest data from remote sensors. However, motion planning in this paradigm needs to maximize the RDG efficiency instead of the navigation efficiency, for which the existing motion planning methods become inefficient, as they plan robot trajectories merely according to motion factors. This paper proposes radio map guided
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Watching Grass Grow: Long-term Visual Navigation and Mission Planning for Autonomous Biodiversity Monitoring arXiv.cs.RO Pub Date : 2024-04-16 Matthew Gadd, Daniele De Martini, Luke Pitt, Wayne Tubby, Matthew Towlson, Chris Prahacs, Oliver Bartlett, John Jackson, Man Qi, Paul Newman, Andrew Hector, Roberto Salguero-Gómez, Nick Hawes
We describe a challenging robotics deployment in a complex ecosystem to monitor a rich plant community. The study site is dominated by dynamic grassland vegetation and is thus visually ambiguous and liable to drastic appearance change over the course of a day and especially through the growing season. This dynamism and complexity in appearance seriously impact the stability of the robotics platform
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Optimizing BioTac Simulation for Realistic Tactile Perception arXiv.cs.RO Pub Date : 2024-04-16 Wadhah Zai El Amri, Nicolás Navarro-Guerrero
Tactile sensing presents a promising opportunity for enhancing the interaction capabilities of today's robots. BioTac is a commonly used tactile sensor that enables robots to perceive and respond to physical tactile stimuli. However, the sensor's non-linearity poses challenges in simulating its behavior. In this paper, we first investigate a BioTac simulation that uses temperature, force, and contact
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FoundationGrasp: Generalizable Task-Oriented Grasping with Foundation Models arXiv.cs.RO Pub Date : 2024-04-16 Chao Tang, Dehao Huang, Wenlong Dong, Ruinian Xu, Hong Zhang
Task-oriented grasping (TOG), which refers to the problem of synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone towards tool manipulation. Analogous to the activation of two brain regions responsible for semantic and geometric reasoning during cognitive processes, modeling the complex relationship between objects, tasks
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Spline-Interpolated Model Predictive Path Integral Control with Stein Variational Inference for Reactive Navigation arXiv.cs.RO Pub Date : 2024-04-16 Takato Miura, Naoki Akai, Kohei Honda, Susumu Hara
This paper presents a reactive navigation method that leverages a Model Predictive Path Integral (MPPI) control enhanced with spline interpolation for the control input sequence and Stein Variational Gradient Descent (SVGD). The MPPI framework addresses a nonlinear optimization problem by determining an optimal sequence of control inputs through a sampling-based approach. The efficacy of MPPI is significantly
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Generating 6-D Trajectories for Omnidirectional Multirotor Aerial Vehicles in Cluttered Environments arXiv.cs.RO Pub Date : 2024-04-16 Peiyan Liu, Yuanzhe Shen, Yueqian Liu, Fengyu Quan, Can Wang, Haoyao Chen
As fully-actuated systems, omnidirectional multirotor aerial vehicles (OMAVs) have more flexible maneuverability and advantages in aggressive flight in cluttered environments than traditional underactuated MAVs. %Due to the high dimensionality of configuration space, making the designed trajectory generation algorithm efficient is challenging. This paper aims to achieve safe flight of OMAVs in cluttered
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ControlMTR: Control-Guided Motion Transformer with Scene-Compliant Intention Points for Feasible Motion Prediction arXiv.cs.RO Pub Date : 2024-04-16 Jiawei Sun, Chengran Yuan, Shuo Sun, Shanze Wang, Yuhang Han, Shuailei Ma, Zefan Huang, Anthony Wong, Keng Peng Tee, Marcelo H. Ang Jr
The ability to accurately predict feasible multimodal future trajectories of surrounding traffic participants is crucial for behavior planning in autonomous vehicles. The Motion Transformer (MTR), a state-of-the-art motion prediction method, alleviated mode collapse and instability during training and enhanced overall prediction performance by replacing conventional dense future endpoints with a small
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Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V arXiv.cs.RO Pub Date : 2024-04-16 Peiyuan Zhi, Zhiyuan Zhang, Muzhi Han, Zeyu Zhang, Zhitian Li, Ziyuan Jiao, Baoxiong Jia, Siyuan Huang
Autonomous robot navigation and manipulation in open environments require reasoning and replanning with closed-loop feedback. We present COME-robot, the first closed-loop framework utilizing the GPT-4V vision-language foundation model for open-ended reasoning and adaptive planning in real-world scenarios. We meticulously construct a library of action primitives for robot exploration, navigation, and
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Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration arXiv.cs.RO Pub Date : 2024-04-16 Jing Zeng, Yanxu Li, Jiahao Sun, Qi Ye, Yunlong Ran, Jiming Chen
Implicit neural representations have demonstrated significant promise for 3D scene reconstruction. Recent works have extended their applications to autonomous implicit reconstruction through the Next Best View (NBV) based method. However, the NBV method cannot guarantee complete scene coverage and often necessitates extensive viewpoint sampling, particularly in complex scenes. In the paper, we propose
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A Probabilistic-based Drift Correction Module for Visual Inertial SLAMs arXiv.cs.RO Pub Date : 2024-04-15 Pouyan Navard, Alper Yilmaz
Positioning is a prominent field of study, notably focusing on Visual Inertial Odometry (VIO) and Simultaneous Localization and Mapping (SLAM) methods. Despite their advancements, these methods often encounter dead-reckoning errors that leads to considerable drift in estimated platform motion especially during long traverses. In such cases, the drift error is not negligible and should be rectified
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Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent Collaboration arXiv.cs.RO Pub Date : 2024-04-16 Benjamin A Newman, Chris Paxton, Kris Kitani, Henny Admoni
Agents that assist people need to have well-initialized policies that can adapt quickly to align with their partners' reward functions. Initializing policies to maximize performance with unknown partners can be achieved by bootstrapping nonlinear models using imitation learning over large, offline datasets. Such policies can require prohibitive computation to fine-tune in-situ and therefore may miss
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Continuous Control Reinforcement Learning: Distributed Distributional DrQ Algorithms arXiv.cs.RO Pub Date : 2024-04-16 Zehao Zhou
Distributed Distributional DrQ is a model-free and off-policy RL algorithm for continuous control tasks based on the state and observation of the agent, which is an actor-critic method with the data-augmentation and the distributional perspective of critic value function. Aim to learn to control the agent and master some tasks in a high-dimensional continuous space. DrQ-v2 uses DDPG as the backbone
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A Longitudinal Study of Child Wellbeing Assessment via Online Interactions with a Social Robots arXiv.cs.RO Pub Date : 2024-04-16 Nida Itrat Abbasi, Guy Laban, Tasmin Ford, Peter B. Jones, Hatice Gunes
Socially Assistive Robots are studied in different Child-Robot Interaction settings. However, logistical constraints limit accessibility, particularly affecting timely support for mental wellbeing. In this work, we have investigated whether online interactions with a robot can be used for the assessment of mental wellbeing in children. The children (N=40, 20 girls and 20 boys; 8-13 years) interacted
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MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints arXiv.cs.RO Pub Date : 2024-04-16 Pengfei Xie, Wenqiang Xu, Tutian Tang, Zhenjun Yu, Cewu Lu
This work proposes a novel learning framework for visual hand dynamics analysis that takes into account the physiological aspects of hand motion. The existing models, which are simplified joint-actuated systems, often produce unnatural motions. To address this, we integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create a new model, MS-MANO. This model emulates the
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AIGeN: An Adversarial Approach for Instruction Generation in VLN arXiv.cs.RO Pub Date : 2024-04-15 Niyati Rawal, Roberto Bigazzi, Lorenzo Baraldi, Rita Cucchiara
In the last few years, the research interest in Vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal. Recent work in literature focuses on different ways to augment the available datasets of instructions for improving navigation performance
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AAM-VDT: Vehicle Digital Twin for Tele-Operations in Advanced Air Mobility arXiv.cs.RO Pub Date : 2024-04-15 Tuan Anh Nguyen, Taeho Kwag, Vinh Pham, Viet Nghia Nguyen, Jeongseok Hyun, Minseok Jang, Jae-Woo Lee
This study advanced tele-operations in Advanced Air Mobility (AAM) through the creation of a Vehicle Digital Twin (VDT) system for eVTOL aircraft, tailored to enhance remote control safety and efficiency, especially for Beyond Visual Line of Sight (BVLOS) operations. By synergizing digital twin technology with immersive Virtual Reality (VR) interfaces, we notably elevate situational awareness and control
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EgoPet: Egomotion and Interaction Data from an Animal's Perspective arXiv.cs.RO Pub Date : 2024-04-15 Amir Bar, Arya Bakhtiar, Danny Tran, Antonio Loquercio, Jathushan Rajasegaran, Yann LeCun, Amir Globerson, Trevor Darrell
Animals perceive the world to plan their actions and interact with other agents to accomplish complex tasks, demonstrating capabilities that are still unmatched by AI systems. To advance our understanding and reduce the gap between the capabilities of animals and AI systems, we introduce a dataset of pet egomotion imagery with diverse examples of simultaneous egomotion and multi-agent interaction.
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Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning arXiv.cs.RO Pub Date : 2024-04-15 Yuan Bi, Cheng Qian, Zhicheng Zhang, Nassir Navab, Zhongliang Jiang
Ultrasound (US) has been widely used in daily clinical practice for screening internal organs and guiding interventions. However, due to the acoustic shadow cast by the subcutaneous rib cage, the US examination for thoracic application is still challenging. To fully cover and reconstruct the region of interest in US for diagnosis, an intercostal scanning path is necessary. To tackle this challenge
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Facial Features Integration in Last Mile Delivery Robots arXiv.cs.RO Pub Date : 2024-04-15 Delgermaa Gankhuyag, Stephanie Groiß, Lena Schwamberger, Özge Talay, Cristina Olaverri-Monreal
Delivery services have undergone technological advancements, with robots now directly delivering packages to recipients. While these robots are designed for efficient functionality, they have not been specifically designed for interactions with humans. Building on the premise that incorporating human-like characteristics into a robot has the potential to positively impact technology acceptance, this
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GuLu XuanYuan , a biomimetic Transformer that intergrates humanoid MIP, reptile UGV, and bird UAV arXiv.cs.RO Pub Date : 2024-04-15 Le Chen, Jie Yu, XingWu Chen
This article proposes a multi habitat bio-mimetic robot, named as GuLu XuanYuan.It combines all common types of mobile robots, namely humanoid MIP, unmanned ground vehicle, and unmanned aerial vehicle. These 3 modals imitate human, bird, and reptile, separately. As a transformer, GuLu XuanYuan can transform from one modal to another. Transforming function integrates the specialized abilities of three
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Enhancing Robot Explanation Capabilities through Vision-Language Models: a Preliminary Study by Interpreting Visual Inputs for Improved Human-Robot Interaction arXiv.cs.RO Pub Date : 2024-04-15 David Sobrín-Hidalgo, Miguel Ángel González-Santamarta, Ángel Manuel Guerrero-Higueras, Francisco Javier Rodríguez-Lera, Vicente Matellán-Olivera
This paper presents an improved system based on our prior work, designed to create explanations for autonomous robot actions during Human-Robot Interaction (HRI). Previously, we developed a system that used Large Language Models (LLMs) to interpret logs and produce natural language explanations. In this study, we expand our approach by incorporating Vision-Language Models (VLMs), enabling the system
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Dynamic Ego-Velocity estimation Using Moving mmWave Radar: A Phase-Based Approach arXiv.cs.RO Pub Date : 2024-04-15 Argha Sen, Soham Chakraborty, Soham Tripathy, Sandip Chakraborty
Precise ego-motion measurement is crucial for various applications, including robotics, augmented reality, and autonomous navigation. In this poster, we propose mmPhase, an odometry framework based on single-chip millimetre-wave (mmWave) radar for robust ego-motion estimation in mobile platforms without requiring additional modalities like the visual, wheel, or inertial odometry. mmPhase leverages
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UAV Navigation in Tunnels with 2D tilted LiDARs arXiv.cs.RO Pub Date : 2024-04-15 Danilo Tardioli, Lorenzo Cano, Alejandro R. Mosteo
Navigation of UAVs in challenging environments like tunnels or mines, where it is not possible to use GNSS methods to self-localize, illumination may be uneven or nonexistent, and wall features are likely to be scarce, is a complex task, especially if the navigation has to be done at high speed. In this paper we propose a novel proof-of-concept navigation technique for UAVs based on the use of LiDAR
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A Generic Trajectory Planning Method for Constrained All-Wheel-Steering Robots arXiv.cs.RO Pub Date : 2024-04-15 Ren Xin, Hongji Liu, Yingbing Chen, Sheng Wang, Ming Liu
This paper presents a trajectory planning method for wheeled robots with fixed steering axes while the steering angle of each wheel is constrained. In the past, All-Wheel-Steering(AWS) robots, incorporating modes such as rotation-free translation maneuvers, in-situ rotational maneuvers, and proportional steering, exhibited inefficient performance due to time-consuming mode switches. This inefficiency
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Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows arXiv.cs.RO Pub Date : 2024-04-15 Georg Rabenstein, Lars Ullrich, Knut Graichen
Alongside optimization-based planners, sampling-based approaches are often used in trajectory planning for autonomous driving due to their simplicity. Model predictive path integral control is a framework that builds upon optimization principles while incorporating stochastic sampling of input trajectories. This paper investigates several sampling approaches for trajectory generation. In this context
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Stiffness-Tuneable Limb Segment with Flexible Spine for Malleable Robots arXiv.cs.RO Pub Date : 2024-04-15 Angus B. Clark, Nicolas Rojas
Robotic arms built from stiffness-adjustable, continuously bending segments serially connected with revolute joints have the ability to change their mechanical architecture and workspace, thus allowing high flexibility and adaptation to different tasks with less than six degrees of freedom, a concept that we call malleable robots. Known stiffening mechanisms may be used to implement suitable links
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Object Instance Retrieval in Assistive Robotics: Leveraging Fine-Tuned SimSiam with Multi-View Images Based on 3D Semantic Map arXiv.cs.RO Pub Date : 2024-04-15 Taichi Sakaguchi, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi, Shoichi Hasegawa, Tadahiro Taniguchi
Robots that assist in daily life are required to locate specific instances of objects that match the user's desired object in the environment. This task is known as Instance-Specific Image Goal Navigation (InstanceImageNav), which requires a model capable of distinguishing between different instances within the same class. One significant challenge in robotics is that when a robot observes the same
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Real-world Instance-specific Image Goal Navigation for Service Robots: Bridging the Domain Gap with Contrastive Learning arXiv.cs.RO Pub Date : 2024-04-15 Taichi Sakaguchi, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi, Shoichi Hasegawa, Tadahiro Taniguchi
Improving instance-specific image goal navigation (InstanceImageNav), which locates the identical object in a real-world environment from a query image, is essential for robotic systems to assist users in finding desired objects. The challenge lies in the domain gap between low-quality images observed by the moving robot, characterized by motion blur and low-resolution, and high-quality query images
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An Origami-Inspired Variable Friction Surface for Increasing the Dexterity of Robotic Grippers arXiv.cs.RO Pub Date : 2024-04-15 Qiujie Lu, Angus B. Clark, Matthew Shen, Nicolas Rojas
While the grasping capability of robotic grippers has shown significant development, the ability to manipulate objects within the hand is still limited. One explanation for this limitation is the lack of controlled contact variation between the grasped object and the gripper. For instance, human hands have the ability to firmly grip object surfaces, as well as slide over object faces, an aspect that
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DIDLM:A Comprehensive Multi-Sensor Dataset with Infrared Cameras, Depth Cameras, LiDAR, and 4D Millimeter-Wave Radar in Challenging Scenarios for 3D Mapping arXiv.cs.RO Pub Date : 2024-04-15 WeiSheng Gong, Chen He, KaiJie Su, QingYong Li
This study presents a comprehensive multi-sensor dataset designed for 3D mapping in challenging indoor and outdoor environments. The dataset comprises data from infrared cameras, depth cameras, LiDAR, and 4D millimeter-wave radar, facilitating exploration of advanced perception and mapping techniques. Integration of diverse sensor data enhances perceptual capabilities in extreme conditions such as
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GeoSACS: Geometric Shared Autonomy via Canal Surfaces arXiv.cs.RO Pub Date : 2024-04-15 Shalutha Rajapakshe, Atharva Dastenavar, Michael Hagenow, Jean-Marc Odobez, Emmanuel Senft
We introduce GeoSACS, a geometric framework for shared autonomy (SA). In variable environments, SA methods can be used to combine robotic capabilities with real-time human input in a way that offloads the physical task from the human. To remain intuitive, it can be helpful to simplify requirements for human input (i.e., reduce the dimensionality), which create challenges for to map low-dimensional
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Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm arXiv.cs.RO Pub Date : 2024-04-15 Luca Beber, Edoardo Lamon, Giacomo Moretti, Daniele Fontanelli, Matteo Saveriano, Luigi Palopoli
This paper introduces a new method for estimating the penetration of the end effector and the parameters of a soft body using a collaborative robotic arm. This is possible using the dimensionality reduction method that simplifies the Hunt-Crossley model. The parameters can be found without a force sensor thanks to the information of the robotic arm controller. To achieve an online estimation, an extended
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SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous Agents arXiv.cs.RO Pub Date : 2024-04-14 Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Muhammad Shafique
Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically powered by portable batteries, they require extremely low power/energy consumption to operate in a long lifespan. To solve this challenge, neuromorphic
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Dynamics of spherical telescopic linear driven rotation robots arXiv.cs.RO Pub Date : 2024-04-14 Jasper Zevering, Dorit Borrmann, Anton Bredenbeck, Andreas Nuechter
Lunar caves are promising features for long-term and permanent human presence on the moon. However, given their inaccessibility to imaging from survey satellites, the concrete environment within the underground cavities is not well known. Thus, to further the efforts of human presence on the moon, these caves are to be explored by robotic systems. However, a set of environmental factors make this exploration
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A Survey on Integration of Large Language Models with Intelligent Robots arXiv.cs.RO Pub Date : 2024-04-14 Yeseung Kim, Dohyun Kim, Jieun Choi, Jisang Park, Nayoung Oh, Daehyung Park
In recent years, the integration of large language models (LLMs) has revolutionized the field of robotics, enabling robots to communicate, understand, and reason with human-like proficiency. This paper explores the multifaceted impact of LLMs on robotics, addressing key challenges and opportunities for leveraging these models across various domains. By categorizing and analyzing LLM applications within
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Design and Fabrication of String-driven Origami Robots arXiv.cs.RO Pub Date : 2024-04-14 Peiwen Yang, Shuguang Li
Origami designs and structures have been widely used in many fields, such as morphing structures, robotics, and metamaterials. However, the design and fabrication of origami structures rely on human experiences and skills, which are both time and labor-consuming. In this paper, we present a rapid design and fabrication method for string-driven origami structures and robots. We developed an origami