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TTCDist: Fast Distance Estimation From an Active Monocular Camera Using Time-to-Contact

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arXiv 2022年

作者： Burner, Levi Sanket, Nitin J. Fermüller, Cornelia Aloimonos, Yiannis Perception and Robotics Group Electrical and Computer Engineering University of Maryland College Park United States Robotics Engineering Worcester Polytechnic Institute United States

Distance estimation from vision is fundamental for a myriad of robotic applications such as navigation, manipulation, and planning. Inspired by the mammal's visual system, which gazes at specific objects, we develop two novel constraints relating time-to-contact, acceleration, and distance that we call the τ-constraint and Φ-constraint. They allow an active (moving) camera to estimate depth efficiently and accurately while using only a small portion of the image. The constraints are applicable to range sensing, sensor fusion, and visual servoing. We successfully validate the proposed constraints with two experiments. The first applies both constraints in a trajectory estimation task with a monocular camera and an Inertial Measurement Unit (IMU). Our methods achieve 30-70% less average trajectory error while running 25× and 6.2× faster than the popular Visual-Inertial Odometry methods VINS-Mono and ROVIO respectively. The second experiment demonstrates that when the constraints are used for feedback with efference copies the resulting closed loop system's eigenvalues are invariant to scaling of the applied control signal. We believe these results indicate the τ and Φ constraint's potential as the basis of robust and efficient algorithms for a multitude of robotic applications. © 2022, CC BY.

关键词： Mammals

Detecting Olives with Synthetic or Real Data? Olive the Above

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Detecting Olives with Synthetic or Real Data? Olive the Abov...

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

作者： Yianni Karabatis Xiaomin Lin Nitin J. Sanket Michail G. Lagoudakis Yiannis Aloimonos Perception and Robotics Group University of Maryland Institute for Advanced Computer Studies University of Maryland College Park MD USA Perception and Autonomous Robotics Group Robotics Engineering Worcester Polytechnic Institute MA USA School of ECE Technical University of Crete Greece

Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a challenge. Labeling several thousands of very dense olive grove images for segmentation is a labor-intensive task. This paper presents a novel approach to detecting olives without the need to manually label data. In this work, we present the world's first olive detection dataset comprised of synthetic and real olive tree images. This is accomplished by generating an auto-labeled photorealistic 3D model of an olive tree. Its geometry is then simplified for lightweight rendering purposes. In addition, experiments are conducted with a mix of synthetically generated and real images, yielding an improvement of up to 66% compared to when only using a small sample of real data. When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection.

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How People Use Active Telepresence Cameras in Tele-manipulation

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How People Use Active Telepresence Cameras in Tele-manipulat...

IEEE International Conference on robotics and Automation (ICRA)

作者： Tsung-Chi Lin Achyuthan Unni Krishnan Zhi Li Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

Robot teleoperation is a reliable way to perform a variety of tasks with complex robotic systems. However, the remote control of active telepresence cameras on the robot for improved telepresence adds an additional degree of complexity while teleoperating and can thus affect the operator’s performance during tele-manipulation. Our previous user study investigates the general human performance and preference when using various wearable cameras. In this paper, we further investigate how humans respond to the usage of telepresence cameras in terms of motion behavior. The findings from our human motion analysis inform several desired designs for robot teleoperation interfaces and assistive autonomy.

关键词： Telepresence Conferences Robot vision systems Cameras Complexity theory Reliability Task analysis

INTEGRATING REINFORCEMENT LEARNING AND AI AGENTS FOR ADAPTIVE ROBOTIC INTERACTION AND ASSISTANCE IN DEMENTIA CARE

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arXiv 2025年

作者： Yuan, Fengpei Hasnaeen, Nehal Zhang, Ran Bible, Bryce Taylor, Joseph Riley Qi, Hairong Yao, Fenghui Zhao, Xiaopeng Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States University of Tennessee KnoxvilleTN37996 United States University of North Carolina CharlotteNC28223 United States Tennessee State University NashvilleTN37209 United States

This study explores a novel approach to advancing dementia care by integrating socially assistive robotics, reinforcement learning (RL), large language models (LLMs), and clinical domain expertise within a simulated environment. This integration addresses the critical challenge of limited experimental data in socially assistive robotics for dementia care, providing a dynamic simulation environment that realistically models interactions between persons living with dementia (PLWDs) and robotic caregivers. The proposed framework introduces a probabilistic model to represent the cognitive and emotional states of PLWDs, combined with an LLM-based behavior simulation to emulate their responses. We further develop and train an adaptive RL system enabling humanoid robots, such as Pepper, to deliver context-aware and personalized interactions and assistance based on PLWDs’ cognitive and emotional states. The framework also generalizes to computer-based agents, highlighting its versatility. Results demonstrate that the RL system, enhanced by LLMs, effectively interprets and responds to the complex needs of PLWDs, providing tailored caregiving strategies. This research contributes to human-computer and human-robot interaction by offering a customizable AI-driven caregiving platform, advancing understanding of dementia-related challenges, and fostering collaborative innovation in assistive technologies. The proposed approach has the potential to enhance the independence and quality of life for PLWDs while alleviating caregiver burden, underscoring the transformative role of interaction-focused AI systems in dementia care. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

Are LEED-certified apartments more satisfying? What do tenants say? 16

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Are LEED-certified apartments more satisfying? What do tenan...

16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020

作者： Patel, Denim Maheshkumar Guo, Xingtong Lee, Kyumin Liu, Shichao Civil and Environmental Engineering Worcester Polytechnic Institute United States Robotics Engineering Worcester Polytechnic Institute United States Computer Science Worcester Polytechnic Institute United States

ISBN: (纸本)9781713823605

Leadership in Energy and Environmental Design (LEED) certified buildings aim at offering more sustainable and healthier places. A few prior have studies that tried to answer the question whether LEED-certified buildings are more satisfying have shown mixed and varying results, based on surveys or questionnaires that might face challenges on sample size and preset questions. In this paper, we aim to understand whether LEED-certified buildings are more positively perceived using a natural language processing approach. We collected 16,761 online user experience reviews on 260 LEED-certified apartments and 180 non-LEED-certified-apartments from social media, then conducted topic modeling and analyzed tenants' satisfaction using sentiment analysis. The overall star-rating indicates that tenants are more satisfied with LEED-certified apartments. In specific, the sentiment analysis has revealed that tenants are more satisfied with the transportation and location, and health and wellbeing, while less satisfied with running cost for LEED-certified apartments, compared to non-LEED-certified counterparts. © 2020 16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020. All rights reserved.

关键词： Sentiment analysis

Light in the Larynx: a Miniaturized Robotic Optical Fiber for In-office Laser Surgery of the Vocal Folds

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arXiv 2022年

作者： Chiluisa, Alex J. Pacheco, Nicholas E. Do, Hoang S. Tougas, Ryan M. Minch, Emily V. Mihaleva, Rositsa Shen, Yao Liu, Yuxiang Carroll, Thomas L. Fichera, Loris The Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Mechanical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Biomedical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Otolaryngology-Head and Neck Surgery Harvard Medical School BostonMA02115 United States

This paper reports the design, construction, and experimental validation of a novel hand-held robot for in-office laser surgery of the vocal folds. In-office endoscopic laser surgery is an emerging trend in Laryngology: It promises to deliver the same patient outcomes of traditional surgical treatment (i.e., in the operating room), at a fraction of the cost. Unfortunately, office procedures can be challenging to perform;the optical fibers used for laser delivery can only emit light forward in a line-of-sight fashion, which severely limits anatomical access. The robot we present in this paper aims to overcome these challenges. The end effector of the robot is a steerable laser fiber, created through the combination of a thin optical fiber (0.225 mm) with a tendon-actuated Nickel-Titanium notched sheath that provides bending. This device can be seamlessly used with most commercially available endoscopes, as it is sufficiently small (1.1 mm) to pass through a working channel. To control the fiber, we propose a compact actuation unit that can be mounted on top of the endoscope handle, so that, during a procedure, the operating physician can operate both the endoscope and the steerable fiber with a single hand. We report simulation and phantom experiments demonstrating that the proposed device substantially enhances surgical access compared to current clinical fibers. Copyright © 2022, The Authors. All rights reserved.

关键词： Endoscopy

Gravity Compensation of the dVRK-Si Patient Side Manipulator based on Dynamic Model Identification

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arXiv 2025年

作者： Zhou, Haoying Yang, Hao Deguet, Anton Fichera, Loris Wu, Jie Ying Kazanzides, Peter Department of Robotics Engineering Worcester Polytechnic Institute MA United States Laboratory for Computational Sensing and Robotics JHU MD United States Department of Computer Science Vanderbilt University TN United States Department of Computer Science Johns Hopkins University MD United States

The da Vinci Research Kit (dVRK, also known as dVRK Classic) is anopen-source teleoperated surgical robotic system whose hardware is obtainedfrom the first generation da Vinci Surgical System (Intuitive, Sunnyvale, CA,USA). The dVRK has greatly facilitated research in robot-assisted surgery overthe past decade and helped researchers address multiple major challenges inthis domain. Recently, the dVRK-Si system, a new version of the dVRK which usesmechanical components from the da Vinci Si Surgical System, became available tothe community. The major difference between the first generation da Vinci andthe da Vinci Si is in the structural upgrade of the Patient Side Manipulator(PSM). Because of this upgrade, the gravity of the dVRK-Si PSM can no longer beignored as in the dVRK Classic. The high gravity offset may lead to relativelylow control accuracy and longer response time. In addition, althoughsubstantial progress has been made in addressing the dynamic modelidentification problem for the dVRK Classic, further research is required onmodel-based control for the dVRK-Si, due to differences in mechanicalcomponents and the demand for enhanced control performance. To address theseproblems, in this work, we present (1) a novel full kinematic model of thedVRK-Si PSM, and (2) a gravity compensation approach based on the dynamic modelidentification. © 2025, CC BY.

关键词： Surgical equipment

Reinforcement Learning of CPG-regulated Locomotion Controller for a Soft Snake Robot

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arXiv 2022年

作者： Liu, Xuan Onal, Cagdas D. Fu, Jie The Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

Intelligent control of soft robots is challenging due to the nonlinear and difficult-to-model dynamics. One promising model-free approach for soft robot control is reinforcement learning (RL). However, model-free RL methods tend to be computationally expensive and data-inefficient and may not yield natural and smooth locomotion patterns for soft robots. In this work, we develop a bio-inspired design of a learning-based goal-tracking controller for a soft snake robot. The controller is composed of two modules: An RL module for learning goal-tracking behaviors given the unmodeled and stochastic dynamics of the robot, and a central pattern generator (CPG) with the Matsuoka oscillators for generating stable and diverse locomotion patterns. We theoretically investigate the maneuverability of Matsuoka CPG’s oscillation bias, frequency, and amplitude for steering control, velocity control, and sim-to-real adaptation of the soft snake robot. Based on this analysis, we proposed a composition of RL and CPG modules such that the RL module regulates the tonic inputs to the CPG system given state feedback from the robot, and the output of the CPG module is then transformed into pressure inputs to pneumatic actuators of the soft snake robot. This design allows the RL agent to naturally learn to entrain the desired locomotion patterns determined by the CPG maneuverability. We validated the optimality and robustness of the control design in both simulation and real experiments, and performed extensive comparisons with state-of-art RL methods to demonstrate the benefit of our bio-inspired control design. © 2022, CC BY-NC-SA.

关键词： Reinforcement learning

ECNNs: Ensemble learning methods for improving planar grasp quality estimation

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arXiv 2021年

作者： Alladkani, Fadi Akl, James Calli, Berk Robotics Engineering Program Worcester Polytechnic Institute 85 Prescott Street WorcesterMA01605 United States

We present an ensemble learning methodology that combines multiple existing robotic grasp synthesis algorithms and obtain a success rate that is significantly better than the individual algorithms. The methodology treats the grasping algorithms as "experts" providing grasp "opinions". An Ensemble Convolutional Neural Network (ECNN) is trained using a Mixture of Experts (MOE) model that integrates these opinions and determines the final grasping decision. The ECNN introduces minimal computational cost overhead, and the network can virtually run as fast as the slowest expert. We test this architecture using open-source algorithms in the literature by adopting GQCNN 4.0, GGCNN and a custom variation of GGCNN as experts and obtained a 6% increase in the grasp success on the Cornell Dataset compared to the best-performing individual algorithm. The performance of the method is also demonstrated using a Franka Emika Panda arm. © 2021, CC BY.

关键词： Learning systems