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 physiolo...
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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.
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...
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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. Work...
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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 s...
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.
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 interacti...
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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,
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 su...
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.
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. Work...
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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.
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 recyc...
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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.
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 obje...
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The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects. However, existing methods train only...
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ISBN:
(数字)9781728171685
ISBN:
(纸本)9781728171692
The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects. However, existing methods train only certain sub-modules using loss functions that often do not correlate with established tracking evaluation measures such as Multi-Object Tracking Accuracy (MOTA) and Precision (MOTP). As these measures are not differentiable, the choice of appropriate loss functions for end-to-end training of multi-object tracking methods is still an open research problem. In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers. As a key ingredient, we propose a Deep Hungarian Net (DHN) module that approximates the Hungarian matching algorithm. DHN allows estimating the correspondence between object tracks and ground truth objects to compute differentiable proxies of MOTA and MOTP, which are in turn used to optimize deep trackers directly. We experimentally demonstrate that the proposed differentiable framework improves the performance of existing multi-object trackers, and we establish a new state of the art on the MOTChallenge benchmark. Our code is publicly available from https://***/yihongXU/deepMOT.
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