arXiv subjects

Michael Gleicher

Publications and source records attributed to Michael Gleicher.

At least 37 records · Page 2Linked to original sources

arXiv2023

Coordinated Multi-Robot Shared Autonomy Based on Scheduling and Demonstrations

Shared autonomy methods, where a human operator and a robot arm work together, have enabled robots to complete a range of complex and highly variable tasks. Existing work primarily focuses on one human sharing autonomy with a single robot. By contrast, in this paper we present an approach for multi-robot shared autonomy that enables one operator to provide real-time corrections across two coordinated robots completing the same task in parallel. Sharing autonomy with multiple robots presents fundamental challenges. The human can only correct one robot at a time, and without coordination, the human may be left idle for long periods of time. Accordingly, we develop an approach that aligns the robot's learned motions to best utilize the human's expertise. Our key idea is to leverage Learning from Demonstration (LfD) and time warping to schedule the motions of the robots based on when they may require assistance. Our method uses variability in operator demonstrations to identify the types of corrections an operator might apply during shared autonomy, leverages flexibility in how quickly the task was performed in demonstrations to aid in scheduling, and iteratively estimates the likelihood of when corrections may be needed to ensure that only one robot is completing an action requiring assistance. Through a preliminary study, we show that our method can decrease the scheduled time spent sanding by iteratively estimating the times when each robot could need assistance and generating an optimized schedule that allows the operator to provide corrections to each robot during these times.

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arXiv2023

Subjective visualization experiences: impact of visual design and experimental design

In contrast to objectively measurable aspects (such as accuracy, reading speed, or memorability), the subjective experience of visualizations has only recently gained importance, and we have less experience how to measure it. We explore how subjective experience is affected by chart design using multiple experimental methods. We measure the effects of changes in color, orientation, and source annotation on the perceived readability and trustworthiness of simple bar charts. Three different experimental designs (single image rating, forced choice comparison, and semi-structured interviews) provide similar but different results. We find that these subjective experiences are different from what prior work on objective dimensions would predict. Seemingly inconsequential choices, like orientation, have large effects for some methods, indicating that study design alters decision-making strategies. Next to insights into the effect of chart design, we provide methodological insights, such as a suggested need to carefully isolate individual elements in charts to study subjective experiences.

cs.HC↗

arXiv2023

Periscope: A Robotic Camera System to Support Remote Physical Collaboration

We investigate how robotic camera systems can offer new capabilities to computer-supported cooperative work through the design, development, and evaluation of a prototype system called Periscope. With Periscope, a local worker completes manipulation tasks with guidance from a remote helper who observes the workspace through a camera mounted on a semi-autonomous robotic arm that is co-located with the worker. Our key insight is that the helper, the worker, and the robot should all share responsibility of the camera view--an approach we call shared camera control. Using this approach, we present a set of modes that distribute the control of the camera between the human collaborators and the autonomous robot depending on task needs. We demonstrate the system's utility and the promise of shared camera control through a preliminary study where 12 dyads collaboratively worked on assembly tasks. Finally, we discuss design and research implications of our work for future robotic camera systems that facilitate remote collaboration.

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arXiv2023

Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms

We provide methods which recover planar scene geometry by utilizing the transient histograms captured by a class of close-range time-of-flight (ToF) distance sensor. A transient histogram is a one dimensional temporal waveform which encodes the arrival time of photons incident on the ToF sensor. Typically, a sensor processes the transient histogram using a proprietary algorithm to produce distance estimates, which are commonly used in several robotics applications. Our methods utilize the transient histogram directly to enable recovery of planar geometry more accurately than is possible using only proprietary distance estimates, and consistent recovery of the albedo of the planar surface, which is not possible with proprietary distance estimates alone. This is accomplished via a differentiable rendering pipeline, which simulates the transient imaging process, allowing direct optimization of scene geometry to match observations. To validate our methods, we capture 3,800 measurements of eight planar surfaces from a wide range of viewpoints, and show that our method outperforms the proprietary-distance-estimate baseline by an order of magnitude in most scenarios. We demonstrate a simple robotics application which uses our method to sense the distance to and slope of a planar surface from a sensor mounted on the end effector of a robot arm.

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arXiv2023

Exploiting Task Tolerances in Mimicry-based Telemanipulation

We explore task tolerances, i.e., allowable position or rotation inaccuracy, as an important resource to facilitate smooth and effective telemanipulation. Task tolerances provide a robot flexibility to generate smooth and feasible motions; however, in teleoperation, this flexibility may make the user's control less direct. In this work, we implemented a telemanipulation system that allows a robot to autonomously adjust its configuration within task tolerances. We conducted a user study comparing a telemanipulation paradigm that exploits task tolerances (functional mimicry) to a paradigm that requires the robot to exactly mimic its human operator (exact mimicry), and assess how the choice in paradigm shapes user experience and task performance. Our results show that autonomous adjustments within task tolerances can lead to performance improvements without sacrificing perceived control of the robot. Additionally, we find that users perceive the robot to be more under control, predictable, fluent, and trustworthy in functional mimicry than in exact mimicry.

cs.RO↗

arXiv2023

Handheld Haptic Device with Coupled Bidirectional Input

Handheld kinesthetic haptic interfaces can provide greater mobility and richer tactile information as compared to traditional grounded devices. In this paper, we introduce a new handheld haptic interface which takes input using bidirectional coupled finger flexion. We present the device design motivation and design details and experimentally evaluate its performance in terms of transparency and rendering bandwidth using a handheld prototype device. In addition, we assess the device's functional performance through a user study comparing the proposed device to a commonly used grounded input device in a set of targeting and tracking tasks.

eess.SY↗

arXiv2023

Exploring the Use of Collaborative Robots in Cinematography

Robotic technology can support the creation of new tools that improve the creative process of cinematography. It is crucial to consider the specific requirements and perspectives of industry professionals when designing and developing these tools. In this paper, we present the results from exploratory interviews with three cinematography practitioners, which included a demonstration of a prototype robotic system. We identified many factors that can impact the design, adoption, and use of robotic support for cinematography, including: (1) the ability to meet requirements for cost, quality, mobility, creativity, and reliability; (2) the compatibility and integration of tools with existing workflows, equipment, and software; and (3) the potential for new creative opportunities that robotic technology can open up. Our findings provide a starting point for future co-design projects that aim to support the work of cinematographers with collaborative robots.

cs.RO↗

arXiv2023

A Problem Space for Designing Visualizations

Visualization researchers and visualization professionals seek appropriate abstractions of visualization requirements that permit considering visualization solutions independently from specific problems. Abstractions can help us design, analyze, organize, and evaluate the things we create. The literature has many task structures (taxonomies, typologies, etc.), design spaces, and related ``frameworks'' that provide abstractions of the problems a visualization is meant to address. In this viewpoint, we introduce a different one, a problem space that complements existing frameworks by focusing on the needs that a visualization is meant to solve. We believe it provides a valuable conceptual tool for designing and discussing visualizations.

cs.HC↗

arXiv2023

RangedIK: An Optimization-based Robot Motion Generation Method for Ranged-Goal Tasks

Generating feasible robot motions in real-time requires achieving multiple tasks (i.e., kinematic requirements) simultaneously. These tasks can have a specific goal, a range of equally valid goals, or a range of acceptable goals with a preference toward a specific goal. To satisfy multiple and potentially competing tasks simultaneously, it is important to exploit the flexibility afforded by tasks with a range of goals. In this paper, we propose a real-time motion generation method that accommodates all three categories of tasks within a single, unified framework and leverages the flexibility of tasks with a range of goals to accommodate other tasks. Our method incorporates tasks in a weighted-sum multiple-objective optimization structure and uses barrier methods with novel loss functions to encode the valid range of a task. We demonstrate the effectiveness of our method through a simulation experiment that compares it to state-of-the-art alternative approaches, and by demonstrating it on a physical camera-in-hand robot that shows that our method enables the robot to achieve smooth and feasible camera motions.

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arXiv2022

Registering Articulated Objects With Human-in-the-loop Corrections

Remotely programming robots to execute tasks often relies on registering objects of interest in the robot's environment. Frequently, these tasks involve articulating objects such as opening or closing a valve. However, existing human-in-the-loop methods for registering objects do not consider articulations and the corresponding impact to the geometry of the object, which can cause the methods to fail. In this work, we present an approach where the registration system attempts to automatically determine the object model, pose, and articulation for user-selected points using nonlinear fitting and the iterative closest point algorithm. When the fitting is incorrect, the operator can iteratively intervene with corrections after which the system will refit the object. We present an implementation of our fitting procedure for one degree-of-freedom (DOF) objects with revolute joints and evaluate it with a user study that shows that it can improve user performance, in measures of time on task and task load, ease of use, and usefulness compared to a manual registration approach. We also present a situated example that integrates our method into an end-to-end system for articulating a remote valve.

cs.RO↗

arXiv2022

A Method For Automated Drone Viewpoints to Support Remote Robot Manipulation

Drones can provide a minimally-constrained adapting camera view to support robot telemanipulation. Furthermore, the drone view can be automated to reduce the burden on the operator during teleoperation. However, existing approaches do not focus on two important aspects of using a drone as an automated view provider. The first is how the drone should select from a range of quality viewpoints within the workspace (e.g., opposite sides of an object). The second is how to compensate for unavoidable drone pose uncertainty in determining the viewpoint. In this paper, we provide a nonlinear optimization method that yields effective and adaptive drone viewpoints for telemanipulation with an articulated manipulator. Our first key idea is to use sparse human-in-the-loop input to toggle between multiple automatically-generated drone viewpoints. Our second key idea is to introduce optimization objectives that maintain a view of the manipulator while considering drone uncertainty and the impact on viewpoint occlusion and environment collisions. We provide an instantiation of our drone viewpoint method within a drone-manipulator remote teleoperation system. Finally, we provide an initial validation of our method in tasks where we complete common household and industrial manipulations.

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arXiv2022

Trinary Tools for Continuously Valued Binary Classifiers

Classification methods for binary (yes/no) tasks often produce a continuously valued score. Machine learning practitioners must perform model selection, calibration, discretization, performance assessment, tuning, and fairness assessment. Such tasks involve examining classifier results, typically using summary statistics and manual examination of details. In this paper, we provide an interactive visualization approach to support such continuously-valued classifier examination tasks. Our approach addresses the three phases of these tasks: calibration, operating point selection, and examination. We enhance standard views and introduce task-specific views so that they can be integrated into a multi-view coordination (MVC) system. We build on an existing comparison-based approach, extending it to continuous classifiers by treating the continuous values as trinary (positive, unsure, negative) even if the classifier will not ultimately use the 3-way classification. We provide use cases that demonstrate how our approach enables machine learning practitioners to accomplish key tasks.

cs.LG↗

arXiv2022

Manually Acquiring Targets from Multiple Viewpoints Using Video Feedback

Objective: The effect of camera viewpoint was studied when performing visually obstructed psychomotor targeting tasks. Background: Previous research in laparoscopy and robotic teleoperation found that complex perceptual-motor adaptations associated with misaligned viewpoints corresponded to degraded performance in manipulation. Because optimal camera positioning is often unavailable in restricted environments, alternative viewpoints that might mitigate performance effects are not obvious. Methods: A virtual keyboard-controlled targeting task was remotely distributed to workers of Amazon Mechanical Turk. The experiment was performed by 192 subjects for a static viewpoint with independent parameters of target direction, Fitts' law index of difficulty, viewpoint azimuthal angle (AA), and viewpoint polar angle (PA). A dynamic viewpoint experiment was also performed by 112 subjects in which the viewpoint AA changed after every trial. Results: AA and target direction had significant effects on performance for the static viewpoint experiment. Movement time and travel distance increased while AA increased until there was a discrete improvement in performance for 180°. Increasing AA from 225° to 315° linearly decreased movement time and distance. There were significant main effects of current AA and magnitude of transition for the dynamic viewpoint experiment. Orthogonal direction and no-change viewpoint transitions least affected performance. Conclusions: Viewpoint selection should aim to minimize associated rotations within the manipulation plane when performing targeting tasks whether implementing a static or dynamic viewing solution. Because PA rotations had negligible performance effects, PA adjustments may extend the space of viable viewpoints. Applications: These results can inform viewpoint-selection for visual feedback during psychomotor tasks.

cs.HC↗

arXiv2022

MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

Recently, there has been a wealth of development in motion planning for robotic manipulation new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker, an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground to accelerate motion planning research.

cs.RO↗

arXiv2021

Task-Level Authoring for Remote Robot Teleoperation

Remote teleoperation of robots can broaden the reach of domain specialists across a wide range of industries such as home maintenance, health care, light manufacturing, and construction. However, current direct control methods are impractical, and existing tools for programming robot remotely have focused on users with significant robotic experience. Extending robot remote programming to end users, i.e., users who are experts in a domain but novices in robotics, requires tools that balance the rich features necessary for complex teleoperation tasks with ease of use. The primary challenge to usability is that novice users are unable to specify complete and robust task plans to allow a robot to perform duties autonomously, particularly in highly variable environments. Our solution is to allow operators to specify shorter sequences of high-level commands, which we call task-level authoring, to create periods of variable robot autonomy. This approach allows inexperienced users to create robot behaviors in uncertain environments by interleaving exploration, specification of behaviors, and execution as separate steps. End users are able to break down the specification of tasks and adapt to the current needs of the interaction and environments, combining the reactivity of direct control to asynchronous operation. In this paper, we describe a prototype system contextualized in light manufacturing and its empirical validation in a user study where 18 participants with some programming experience were able to perform a variety of complex telemanipulation tasks with little training. Our results show that our approach allowed users to create flexible periods of autonomy and solve rich manipulation tasks. Furthermore, participants significantly preferred our system over comparative more direct interfaces, demonstrating the potential of our approach.

cs.RO↗

arXiv2021

Recognizing Orientation Slip in Human Demonstrations

Manipulations of a constrained object often use a non-rigid grasp that allows the object to rotate relative to the end effector. This orientation slip strategy is often present in natural human demonstrations, yet it is generally overlooked in methods to identify constraints from such demonstrations. In this paper, we present a method to model and recognize prehensile orientation slip in human demonstrations of constrained interactions. Using only observations of an end effector, we can detect the type of constraint, parameters of the constraint, and orientation slip properties. Our method uses a novel hierarchical model selection method that is informed by multiple origins of physics-based evidence. A study with eight participants shows that orientation slip occurs in natural demonstrations and confirms that it can be detected by our method.

cs.RO↗

arXiv2021

Situated Live Programming for Human-Robot Collaboration

We present situated live programming for human-robot collaboration, an approach that enables users with limited programming experience to program collaborative applications for human-robot interaction. Allowing end users, such as shop floor workers, to program collaborative robots themselves would make it easy to "retask" robots from one process to another, facilitating their adoption by small and medium enterprises. Our approach builds on the paradigm of trigger-action programming (TAP) by allowing end users to create rich interactions through simple trigger-action pairings. It enables end users to iteratively create, edit, and refine a reactive robot program while executing partial programs. This live programming approach enables the user to utilize the task space and objects by incrementally specifying situated trigger-action pairs, substantially lowering the barrier to entry for programming or reprogramming robots for collaboration. We instantiate situated live programming in an authoring system where users can create trigger-action programs by annotating an augmented video feed from the robot's perspective and assign robot actions to trigger conditions. We evaluated this system in a study where participants (n = 10) developed robot programs for solving collaborative light-manufacturing tasks. Results showed that users with little programming experience were able to program HRC tasks in an interactive fashion and our situated live programming approach further supported individualized strategies and workflows. We conclude by discussing opportunities and limitations of the proposed approach, our system implementation, and our study and discuss a roadmap for expanding this approach to a broader range of tasks and applications.

cs.RO↗

arXiv2021

Informing Real-time Corrections in Corrective Shared Autonomy Through Expert Demonstrations

Corrective Shared Autonomy is a method where human corrections are layered on top of an otherwise autonomous robot behavior. Specifically, a Corrective Shared Autonomy system leverages an external controller to allow corrections across a range of task variables (e.g., spinning speed of a tool, applied force, path) to address the specific needs of a task. However, this inherent flexibility makes the choice of what corrections to allow at any given instant difficult to determine. This choice of corrections includes determining appropriate robot state variables, scaling for these variables, and a way to allow a user to specify the corrections in an intuitive manner. This paper enables efficient Corrective Shared Autonomy by providing an automated solution based on Learning from Demonstration to both extract the nominal behavior and address these core problems. Our evaluation shows that this solution enables users to successfully complete a surface cleaning task, identifies different strategies users employed in applying corrections, and points to future improvements for our solution.

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