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IEEE International Conference on Soft robotics (RoboSoft)

作者： Joseph DelPreto Cheryl L. Brunelle Alphonse G. Taghian Daniela Rus MIT Distributed Robotics Lab Cambridge MA USA Massachusetts General Hospital Boston MA USA

Smart soft wearable devices have great potential to change how technology is integrated into daily life. A particularly impactful and growing application is continuous medical monitoring; being able to stream physiological and behavioral information creates personalized datasets that can lead to more tailored treatments, diagnoses, and research. An area that can greatly benefit from these developments is lymphedema management, which aims to prevent a potentially irreversible swelling of limbs due to causes such as breast cancer surgeries. Compression sleeves are the state of the art for treatment, but many open questions remain regarding effective pressure and usage prescriptions. To help address these, this work presents a soft pressure sensor, a way to integrate it into wearable devices, and sensorized compression sleeves that continuously monitor pressure and usage. There are significant challenges to developing sensors for high-pressure applications on the human body, including operating between soft compliant interfaces, being safe and unobtrusive, and reducing calibration for new users. This work compares two sensing approaches for wearable applications: a custom pouch-based pneumatic sensor, and a commercially available resistive sensor. Experiments systematically explore design considerations including sensitivity to ambient temperature and pressure, characterize sensor response curves, and evaluate expected accuracies and required calibrations. Sensors are then integrated into compression sleeves and worn for over 115 hours spanning 10 days.

关键词： Temperature sensors Temperature measurement Wearable computers Sensor phenomena and characterization Robot sensing systems Sensors Calibration

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ODANet: Online Deep Appearance Network for Identity-Consistent Multi-person Tracking 25th

ODANet: Online Deep Appearance Network for Identity-Consiste...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Delorme, Guillaume Ban, Yutong Sarrazin, Guillaume Alameda-Pineda, Xavier Inria Univ. Grenoble-Alpes LJK CNRS Paris France MIT CSAIL Distributed Robotics Lab Cambridge United States

ISBN: (纸本)9783030687793

The analysis of affective states through time in multi-person scenarii is very challenging, because it requires to consistently track all persons over time. This requires a robust visual appearance model capable of re-identifying people already tracked in the past, as well as spotting newcomers. In real-world applications, the appearance of the persons to be tracked is unknown in advance, and therefore one must devise methods that are both discriminative and flexible. Previous work in the literature proposed different tracking methods with fixed appearance models. These models allowed, up to a certain extend, to discriminate between appearance samples of two different people. We propose online deep appearance network (ODANet), a method able to track people, and simultaneously update the appearance model with the newly gathered annotation-less images. Since this task is specially relevant for autonomous systems, we also describe a platform-independent robotic implementation of ODANet. Our experiments show the superiority of the proposed method with respect to the state of the art, and demonstrate the ability of ODANet to adapt to sudden changes in appearance, to integrate new appearances in the tracking system and to provide more identity-consistent tracks. © 2021, Springer Nature Switzerland AG.

关键词： Computers

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TelePulse: Enhancing the Teleoperation Experience through Biomechanical Simulation-Based Electrical Muscle Stimulation in Virtual Reality 25

TelePulse: Enhancing the Teleoperation Experience through Bi...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Seokhyun Hwang Seongjun Kang Jeongseok Oh Jeongju Park Semoo Shin Yiyue Luo Joseph DelPreto Sangbeom Lee Kyoobin Lee Wojciech Matusik Daniela Rus SeungJun Kim Information School University of Washington Seattle Washington USA and Human-Centered Intelligent Systems Lab Gwangju Institute of Science and Technology Gwangju Republic of Korea Human-Centered Intelligent Systems Lab Gwangju Institute of Science and Technology Gwangju Republic of Korea ECE University of Washington Seattle Washington USA Distributed Robotics Lab Massachusetts Institute of Technology (MIT) Cambridge Massachusetts USA Artificial Intelligence Lab Gwangju Institute of Science and Technology Gwangju Republic of Korea CSAIL MIT Cambridge Massachusetts USA Distributed Robotics Lab Massachusetts Institute of Technology Cambridge Massachusetts USA

ISBN: (纸本)9798400713941

This paper introduces TelePulse, a system integrating biomechanical simulation with electrical muscle stimulation (EMS) to provide precise haptic feedback for robot teleoperation tasks in virtual reality (VR). TelePulse has two components: a physical simulation part that calculates joint torques based on real-time force data from remote manipulators, and an electrical stimulation part that converts these torques into muscle stimulation. Two experiments were conducted to evaluate the system. The first experiment assessed the accuracy of EMS generated through biomechanical simulations by comparing it with electromyography (EMG) data during force-directed tasks, while the second experiment evaluated the impact of TelePulse on teleoperation performance during sanding and drilling tasks. The results suggest that TelePulse provided more accurate stimulation across all arm muscles, thereby enhancing task performance and user experience in the teleoperation environment. In this paper, we discuss the effect of TelePulse on teleoperation, its limitations, and areas for future improvement. © 2025 Copyright held by the owner/author(s).

关键词： Teleoperation Virtual Reality Haptic Electrical Muscle Stimulation Biomechanics Simulation Wearable Device

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Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation

Helping Robots Learn: A Human-Robot Master-Apprentice Model ...

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2020 IEEE International Conference on robotics and Automation, ICRA 2020

作者： Delpreto, Joseph Lipton, Jeffrey I. Sanneman, Lindsay Fay, Aidan J. Fourie, Christopher Choi, Changhyun Rus, Daniela Mit Distributed Robotics Lab CambridgeMA02139 United States

ISBN: (纸本)9781728173955

As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human's time and expertise while facilitating future scalability to supervision of multiple robots;the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach;100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot's skill and to generalize its capabilities, especially as learning improves. © 2020 IEEE.

关键词： Demonstrations

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Mechanically Programmable, Degradable & Ingestible Soft Actuators

Mechanically Programmable, Degradable & Ingestible Soft Actu...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Josie Hughes Daniela Rus Distributed Robotics Lab CSAIL MIT

ISBN: (数字)9781728165707

ISBN: (纸本)9781728165714

One of the key challenges in soft robotics is the development of actuators which are truly soft and compliant, and can be adapted and tailored for different applications. In particular, the development of untethered soft actuators could enable robots to autonomously explore the world in an unrestricted manner, exploiting their compliant behavior. In this paper we present a method for creating fully soft, degradable actuators where the actuation of the system is controlled by setting physical parameters which `mechanically program' the actuator determining the characteristics of the actuator. The actuation process is driven by the release of gas from a reaction between a bio-compatible acid and base. This approach allows for the creation of fully untethered actuators which could be deployed for use in agriculture, to make ingestible robots or to allow untethered exploration. This paper provides the `recipes' for the development of the actuators used, and the methods for mechanically programming of the actuators.

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Plug-and-Play Gesture Control Using Muscle and Motion Sensors

Plug-and-Play Gesture Control Using Muscle and Motion Sensor...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Joseph DelPreto Daniela Rus MIT Distributed Robotics Lab Cambridge Massachusetts

As the capacity for machines to extend human capabilities continues to grow, the communication channels used must also expand. Allowing machines to interpret nonverbal commands such as gestures can help make interactions more similar to interactions with another person. Yet to be pervasive and effective in realistic scenarios, such interfaces should not require significant sensing infrastructure or per-user setup time. The presented work takes a step towards these goals by using wearable muscle and motion sensors to detect gestures without dedicated calibration or training procedures. An algorithm is presented for clustering unlabeled streaming data in real time, and it is applied to adaptively thresholding muscle and motion signals acquired via electromyography (EMG) and an inertial measurement unit (IMU). This enables plug-and-play online detection of arm stiffening, fist clenching, rotation gestures, and forearm activation. It also augments a neural network pipeline, trained only on strategically chosen training data from previous users, to detect left, right, up, and down gestures. Together, these pipelines offer a plug-and-play gesture vocabulary suitable for remotely controlling a robot. Experiments with 6 subjects evaluate classifier performance and interface efficacy. Classifiers correctly identified 97.6% of 1,200 cued gestures, and a drone correctly responded to 81.6% of 1,535 unstructured gestures as subjects remotely controlled it through target hoops during 119 minutes of total flight time. CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI); Gestural input; • Computer systems organization →robotics; • Computing methodologies→Machine learning. ACM Reference Format: Joseph DelPreto and Daniela Rus. 2020. Plug-and-Play Gesture Control Using Muscle and Motion Sensors. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20), March 23–26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA,

关键词： Human computer interaction Vocabulary Pipelines Neural networks Human-robot interaction Training data Muscles

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Extensible High Force Manipulator For Complex Exploration

Extensible High Force Manipulator For Complex Exploration

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Josie Hughes Cosmio Della Santina Daniela Rus Distributed Robotics Lab CSAIL MIT.

ISBN: (数字)9781728165707

ISBN: (纸本)9781728165714

The development of compliant robotic manipulators which can show length change, compliance and dexterity could assist many challenging applications. Potential applications range from dexterous manipulation, robotic surgery or exploration of challenging environments. Despite significant developments in both fabrication and control approaches for continuum body manipulators, there have been few demonstrations of continuum body systems which display all these properties. We present a method for fabricating a continuum manipulation which shows extension, high force movements and a range of dexterous position. This approach uses 3D printing to create a flexible rack and pinion system. These high torque mechanisms are mounted at points along the 3D printed tracks to allow complex shape control of the continuum system. A controller has been also been developed based on a Piecewise Constant Curvature approximation to allow the position of the tip of the manipulator to be controlled, and motion paths to be followed. In this work, we show the force capabilities of this manipulator and demonstrate how multiple segments can be created for more complex movements.

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Sensorization of a Continuum Body Gripper for High Force and Delicate Object Grasping

Sensorization of a Continuum Body Gripper for High Force and...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Josie Hughes Shuguang Li Daniela Rus Distributed Robotics Lab CSAIL MIT United States

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

The goal of achieving `universal grasping' where many objects can be handled with minimal control input is the focus of much research due to potential high impact applications ranging from grocery packing to recycling. However, many of the grippers developed suffer from limited sensing capabilities which can prevent handing of both heavy bulky items and also lightweight delicate objects which require fine control when grasping. Sensorizing such grippers is often challenging due to the highly deformable surfaces. We propose a novel sensing approach which uses highly flexible latex bladders. By measuring changes in the air pressure of the bladders, normal force and longitudinal strain can be measured. These sensors have been integrated into a `Magic Ball' origami gripper to provide both tactile and proprioceptive sensing. The sensors show reasonable sensitivity and repeatability, are durable and low-cost, and can be easily integrated into the gripper without affecting performance. When the sensors are used for classification, they enabled identification of 10 objects with over 90% accuracy, and also allow failure to be detected through slippage detection. A control algorithm has been developed which uses the sensor feedback to extend the capabilities of the gripper to include both delicate and strong grasping. It is shown that this closed loop controller enables delicate grasping of potato chips; 80% of those tested were grasped without damage.

关键词： Grippers Force Robot sensing systems Strain Bladder Mechanical sensors

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Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation

Helping Robots Learn: A Human-Robot Master-Apprentice Model ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Joseph DelPreto Jeffrey I. Lipton Lindsay Sanneman Aidan J. Fay Christopher Fourie Changhyun Choi Daniela Rus MIT Distributed Robotics Lab Cambridge MA MIT Interactive Robotics Group Cambridge MA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human's time and expertise while facilitating future scalability to supervision of multiple robots; the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach; 100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot's skill and to generalize its capabilities, especially as learning improves.

关键词： Robots Grasping Task analysis Three-dimensional displays Solid modeling Virtual reality Pipelines

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TransCenter: Transformers with Dense Representations for Multiple-Object Tracking

arXiv

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arXiv 2021年

作者： Xu, Yihong Ban, Yutong Delorme, Guillaume Gan, Chuang Rus, Daniela Alameda-Pineda, Xavier Inria Grenoble Rhône-Alpes Montbonnot Saint-Martin France Distributed Robotics Lab CSAIL Massachusetts Institute of Technology United States MIT-IBM Watson AI Lab United States

Transformers have proven superior performance for a wide variety of tasks since they were introduced. In recent years, they have drawn attention from the vision community in tasks such as image classification and object detection. Despite this wave, an accurate and efficient multiple-object tracking (MOT) method based on transformers is yet to be designed. We argue that the direct application of a transformer architecture with quadratic complexity and insufficient noise-initialized sparse queries – is not optimal for MOT. We propose TransCenter, a transformer-based MOT architecture with dense representations for accurately tracking all the objects while keeping a reasonable runtime. Methodologically, we propose the use of image-related dense detection queries and efficient sparse tracking queries produced by our carefully designed query learning networks (QLN). On one hand, the dense image-related detection queries allow us to infer targets’ locations globally and robustly through dense heatmap outputs. On the other hand, the set of sparse tracking queries efficiently interacts with image features in our TransCenter Decoder to associate object positions through time. As a result, TransCenter exhibits remarkable performance improvements and outperforms by a large margin the current state-of-the-art methods in two standard MOT benchmarks with two tracking settings (public/private). TransCenter is also proven efficient and accurate by an extensive ablation study and comparisons to more naive alternatives and concurrent works. For scientific interest, the code is made publicly available at https://***/yihongxu/transcenter. © 2021, CC BY-NC-ND.

关键词： Object detection

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