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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Stefan Fabian Oskar von Stryk Computer Science Department Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Germany

The remote human operator's user interface (UI) is an important link to make the robot an efficient extension of the operator's perception and action. In rescue applications, several studies have investigated the design of operator interfaces based on observations during major robotics competitions or field deployments. Based on this research, guidelines for good interface design were empirically identified. The investigations on the UIs of teams participating in competitions are often based on external observations during UI application, which may miss some relevant requirements for UI flexibility. In this work, we present an open-source and flexibly configurable user interface based on established guidelines and its exemplary use for wheeled, tracked, and walking robots. We explain the design decisions and cover the insights we have gained during its highly successful applications in multiple robotics competitions and evaluations. The presented UI can also be adapted for other robots with little effort and is available as open source.

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Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistance

Fairness-Sensitive Policy-Gradient Reinforcement Learning fo...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jie Zhu Mengsha Hu Amy Zhang Ruoming Jin Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science George Washington University Department of Computer Science Kent State University

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robots assist humans in various activities, from daily living to collaborative manufacturing. Because they have biased learning sources (e.g., data, demonstrations, human feedback), robots inevitably have discriminatory performance regarding individual differences (e.g., skin color, mobility, appearance); discriminatory performance will undermine robots’ service quality, causes request ignorance and response delay, and even cause emotional offenses. Therefore, mitigating biases is critically important for delivering fair robotic services. In this paper, we design a bias-mitigation method – Fairness-Sensitive Policy Gradient Reinforcement Learning (FSPGRL), to help robots self-identify and correct biased behaviors. FSP-GRL identifies bias by examining the abnormal updates along particular gradients and updates the policy network to provide fair decisions. To validate FSPGRL’s effectiveness, we designed a human-centered service scenario: a robot serving people in a restaurant. With a user study involving 24 humans and 1,000 service demonstrations, FSPGRL has proven effective in maintaining fairness during robot services.

关键词： Image color analysis Design methodology Collaboration Reinforcement learning Skin Manufacturing Delays Robots

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VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model 41

VoroNav: Voronoi-based Zero-shot Object Navigation with Larg...

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41st International Conference on Machine Learning, ICML 2024

作者： Wu, Pengying Mu, Yao Wu, Bingxian Hou, Yi Ma, Ji Zhang, Shanghang Liu, Chang Department of Advancded Manufacturing and Robotics College of Engineering Peking University Beijing China The University of Hong Kong Hong Kong OpenGVLab Shanghai AI Laboratory China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China

In the realm of household robotics, the Zero-Shot Object Navigation (ZSON) task empowers agents to adeptly traverse unfamiliar environments and locate objects from novel categories without prior explicit training. This paper introduces VoroNav, a novel semantic exploration framework that proposes the Reduced Voronoi Graph to extract exploratory paths and planning nodes from a semantic map constructed in real time. By harnessing topological and semantic information, VoroNav designs text-based descriptions of paths and images that are readily interpretable by a large language model (LLM). In particular, our approach presents a synergy of path and farsight descriptions to represent the environmental context, enabling LLM to apply commonsense reasoning to ascertain waypoints for navigation. Extensive evaluation on HM3D and HSSD validates VoroNav surpasses existing benchmarks in both success rate and exploration efficiency (absolute improvement: +2.8% Success and +3.7% SPL on HM3D, +2.6% Success and +3.8% SPL on HSSD). Additionally introduced metrics that evaluate obstacle avoidance proficiency and perceptual efficiency further corroborate the enhancements achieved by our method in ZSON planning. Copyright 2024 by the author(s)

关键词： Semantics

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Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction

Signs of Language: Embodied Sign Language Fingerspelling Acq...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Federico Tavella Aphrodite Galata Angelo Cangelosi Centre for Robotics and AI University of Manchester UK Department of Computer Science University of Manchester UK

Learning fine-grained movements is a challenging topic in robotics, particularly in the context of robotic hands. One specific instance of this challenge is the acquisition of fingerspelling sign language in robots. In this paper, we propose an approach for learning dexterous motor imitation from video examples without additional information. To achieve this, we first build a URDF model of a robotic hand with a single actuator for each joint. We then leverage pre-trained deep vision models to extract the 3D pose of the hand from RGB videos. Next, using state-of-the-art reinforcement learning algorithms for motion imitation (namely, proximal policy optimization and soft actor-critic), we train a policy to reproduce the movement extracted from the demonstrations. We identify the optimal set of hyperparameters for imitation based on a reference motion. Finally, we demonstrate the generalizability of our approach by testing it on six different tasks, corresponding to fingerspelled letters. Our results show that our approach is able to successfully imitate these fine-grained movements without additional information, highlighting its potential for real-world applications in robotics.

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Direct Shear Force Measurement by a GaN Nanopillar LED Based Tactile Sensor

Direct Shear Force Measurement by a GaN Nanopillar LED Based...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Dvořák, Nathan Yi, Xili Fazeli, Nima Ku, Pei-Cheng Department of Electrical Engineering and Computer Science University of Michigan 1301 Beal Ave Ann ArborMI48109-2122 United States Department of Robotics University of Michigan 2505 Hayward St Ann ArborMI48109-2122 United States

The response of a GaN nanopillar based tactile sensor to a shear force is investigated. Two different materials were used to apply the force to understand how the tactile sensor responds to different slipping conditio... 详细信息

ISBN: (纸本)9781957171258

关键词： III-V semiconductors

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Invisible Servoing: A Visual Servoing Approach with Return-Conditioned Latent Diffusion

Invisible Servoing: A Visual Servoing Approach with Return-C...

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2025 International Conference on Unmanned Aircraft Systems, ICUAS 2025

作者： Gerges, Bishoy Bazzana, Barbara Botteghi, Nicolò Aboudorra, Youssef Franchi, Antonio University of Twente Department of Applied Mathematics Enschede 7500AE Netherlands University of Twente Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics and Computer Science Enschede 7500AE Netherlands Sapienza University of Rome Department of Computer Control and Management Engineering Rome 00185 Italy

ISBN: (纸本)9798331513283

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE. © 2025 IEEE.

关键词： Target drones

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Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

arXiv

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arXiv 2023年

作者： Deng, Yuhong Guo, Xiaofeng Wei, Yixuan Lu, Kai Fang, Bin Guo, Di Liu, Huaping Sun, Fuchun Department of Computer Science and Technology Tsinghua University Beijing China Shenzhen Academy of Robotics China

In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for the current affordance map, and a deep Q-Network (DQN) is employed to guide the robotic hand to actively explore the environment until the generated affordance map is suitable for grasping. Experimental results have demonstrated that the proposed robotic grasping system is able to greatly increase the success rate of the robotic grasping in cluttered scenes. Copyright © 2023, The Authors. All rights reserved.

关键词： Grippers

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Visual-Kinematics Graph Learning for Procedure-Agnostic Instrument Tip Segmentation in Robotic Surgeries

Visual-Kinematics Graph Learning for Procedure-Agnostic Inst...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jiaqi Liu Yonghao Long Kai Chen Cheuk Hei Leung Zerui Wang Qi Dou Department of Computer Science and Engineering The Chinese University of Hong Kong Cornerstone Robotics Ltd.

Accurate segmentation of surgical instrument tip is an important task for enabling downstream applications in robotic surgery, such as surgical skill assessment, tool-tissue interaction and deformation modeling, as well as surgical autonomy. However, this task is very challenging due to the small sizes of surgical instrument tips, and significant variance of surgical scenes across different procedures. Although much effort has been made on visual-based methods, existing segmentation models still suffer from low robustness thus not usable in practice. Fortunately, kinematics data from the robotic system can provide reliable prior for instrument location, which is consistent regardless of different surgery types. To make use of such multi-modal information, we propose a novel visual-kinematics graph learning framework to accurately segment the instrument tip given various surgical procedures. Specifically, a graph learning framework is proposed to encode relational features of instrument parts from both image and kinematics. Next, a cross-modal contrastive loss is designed to incorporate robust geometric prior from kinematics to image for tip segmentation. We have conducted experiments on a private paired visual-kinematics dataset including multiple procedures, i.e., prostatectomy, total mesorectal excision, fundoplication and distal gastrectomy on cadaver, and distal gastrectomy on porcine. The leave-one-procedure-out cross validation demon-strated that our proposed multi-modal segmentation method significantly outperformed current image-based state-of-the-art approaches, exceeding averagely 11.2% on Dice.

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Explicit-Implicit Subgoal Planning for Long-Horizon Tasks With Sparse Rewards

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IEEE Transactions on Automation science and Engineering 2025年 22卷 16038-16049页

作者： Wang, Fangyuan Duan, Anqing Zhou, Peng Huo, Shengzeng Guo, Guodong Yang, Chenguang Navarro-Alarcon, David The Hong Kong Polytechnic University Department of Mechanical Engineering Hung Hom Hong Kong Ningbo Institute of Digital Twin Ningbo315200 China Robotics Department Abu Dhabi United Arab Emirates Great Bay University School of Advanced Engineering Dongguan523000 China Ningbo Institute of Digital Twin Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin Ningbo315200 China University of Liverpool Department of Computer Science LiverpoolL69 7ZX United Kingdom

The challenges inherent in long-horizon tasks in robotics persist due to the typical inefficient exploration and sparse rewards in traditional reinforcement learning approaches. To address these challenges, we have developed a novel algorithm, termed hlexplicit-implicit subgoal planning (EISP), designed to tackle long-horizon tasks through a divide-and-conquer approach. We utilize two primary criteria, feasibility and optimality, to ensure the quality of the generated subgoals. EISP consists of three components: a hybrid subgoal generator, a hindsight sampler, and a value selector. The hybrid subgoal generator uses an explicit model to infer subgoals and an implicit model to predict the final goal, inspired by way of human thinking that infers subgoals by using the current state and final goal as well as reason about the final goal conditioned on the current state and given subgoals. Additionally, the hindsight sampler selects valid subgoals from an offline dataset to enhance the feasibility of the generated subgoals. While the value selector utilizes the value function in reinforcement learning to filter the optimal subgoals from subgoal candidates. To validate our method, we conduct four long-horizon tasks in both simulation and the real world. The obtained quantitative and qualitative data indicate that our approach achieves promising performance compared to other baseline methods. © 2004-2012 IEEE.

关键词： Reinforcement learning

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Research of Self-organization Processes in Socio-technical Multivariable Dynamic Systems 3

Research of Self-organization Processes in Socio-technical M...

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3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Ilyasov, Barri Gerasimova, Ilmira Karamzina, Anastasia Ufa State Aviation Technical University Department of Computer Science and Robotics Ufa Russia

ISBN: (纸本)9781665439817

Various structures of self-organization processes in multivariable dynamic control systems of socio-technical dynamic objects are considered on the example of scientific schools and educational systems. The study of the mechanism for the emergence of synergistic properties in a given class of systems is carried out at the level of their mathematical models, which are a set of interconnected nonlinear differential equations. The description of a multivariable control system is based on its structural and functional decomposition into separate real subsystems and multidimensional connections between them. This description allowed us to identify some regularities in the emergence of synergistic properties. Numerical examples are given showing the advantages of this research technology. © 2021 IEEE.

关键词： Differential equations

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