Author name cluster

Michael Young

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5 papers

2 author rows

IROS Conference 2021 Conference Paper

An Analysis of Human-Robot Information Streams to Inform Dynamic Autonomy Allocation

Christopher X. Miller
Temesgen Gebrekristos
Michael Young
Enid Montague
Brenna Argall

A dynamic autonomy allocation framework automatically shifts how much control lies with the human versus the robotics autonomy, for example based on factors such as environmental safety or user preference. To investigate the question of which factors should drive dynamic autonomy allocation, we perform a human subject study to collect ground truth data that shifts between levels of autonomy during shared-control robot operation. Information streams from the human, the interaction between the human and the robot, and the environment are analyzed. Machine learning methods—both classical and deep learning—are trained on this data. An analysis of information streams from the human-robot team suggests features which capture the interaction between the human and the robotics autonomy are the most informative in predicting when to shift autonomy levels. Even the addition of data from the environment does little to improve upon this predictive power. The features learned by deep networks, in comparison to the hand-engineered features, prove variable in their ability to represent shift-relevant information. This work demonstrates the classification power of human-only and human-robot interaction information streams for use in the design of shared-control frameworks, and provides insights into the comparative utility of various data streams and methods to extract shift-relevant information from those data.

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IROS Conference 2019 Conference Paper

Discrete N-Dimensional Entropy of Behavior: DNDEB

Michael Young
Mahdieh Nejati Javaremi
Brenna Argall

Shared control for human-robot teams—where both the human and the robot’s autonomy provide commands to the hardware—offers advantages over fully teleoperated or fully autonomous systems by utilizing the unique skill sets of both the human and robot’s autonomy simultaneously. However, the mechanism by which control is shared is often static and many teams could benefit from adjusting this mechanism, such that the human or autonomy alternatively receive more control authority in different scenarios. The question then is: how do we know when these scenarios occur? In this paper, we present a method to estimate the performance of human-robot teams using a novel metric called Discrete N-Dimensional Entropy of Behavior (DNDEB). DNDEB utilizes knowledge of a high-performing human-robot team to build a model of how the team should operate. The model is used to predict the human’s command. The error between the prediction and actual command is tracked and after a certain number of samples, entropy is estimated. A higher level of entropy corresponds to deviations from the high-performance model, which can be interpreted as poor performance by the human-robot team (e. g. , long task time or a collision). Our formulation offers several advantages: it (1) accepts discrete inputs of any size, (2) does not require additional sensors, and (3) is tunable to the specific application. To validate this, we conduct a 15person study where subjects operated a powered wheelchair under three different shared-control paradigms. We find that entropy is higher for cases with longer task durations and cases where there is a collision. Moreover, we use DNDEB thresholds as a mechanism to predict the performance of the human-robot team online and find an average accuracy of 91% with a prescience rate of 72%.

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ICRA Conference 2019 Conference Paper

Formalized Task Characterization for Human-Robot Autonomy Allocation

Michael Young
Christopher X. Miller
Youyi Bi
Wei Chen 0041
Brenna Argall

Humans and robots team together to perform tasks in various domains. Some tasks are easier to perform than others, but little work focuses on discovering the underlying mechanisms that affect perceived difficulty and task performance. To fill this gap, we propose a formalized approach to task characterization for human-robot teams using Taguchi design of experiments and conjoint analysis. With this, we conduct a 20 person study where participants operate a 6 degree of freedom robotic arm to perform manipulations defined by 6 kinematic features. We find that rotational features of a task contribute significantly more to decreased performance and increased difficulty than translational features. The participants also perform the activities with autonomy assistance. The data shows a reduction in the effect of these features on performance and difficulty when assistance is active. Furthermore, we examine when to trigger assistance based on thresholds set from outlier detection. The analysis indicates that rotational features and features leading to kinematic singularities are useful for triggering assistance.

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NeurIPS Conference 2015 Conference Paper

Hidden Technical Debt in Machine Learning Systems

D. Sculley
Gary Holt
Daniel Golovin
Eugene Davydov
Todd Phillips
Dietmar Ebner
Vinay Chaudhary
Michael Young

Machine learning offers a fantastically powerful toolkit for building useful complexprediction systems quickly. This paper argues it is dangerous to think ofthese quick wins as coming for free. Using the software engineering frameworkof technical debt, we find it is common to incur massive ongoing maintenancecosts in real-world ML systems. We explore several ML-specific risk factors toaccount for in system design. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configurationissues, changes in the external world, and a variety of system-level anti-patterns.

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ICML Conference 2013 Conference Paper

Large-Scale Learning with Less RAM via Randomization

Daniel Golovin
D. Sculley
H. Brendan McMahan
Michael Young

We reduce the memory footprint of popular large-scale online learning methods by projecting our weight vector onto a coarse discrete set using randomized rounding. Compared to standard 32-bit float encodings, this reduces RAM usage by more than 50% during training and by up 95% when making predictions from a fixed model, with almost no loss in accuracy. We also show that randomized counting can be used to implement per-coordinate learning rates, improving model quality with little additional RAM. We prove these memory-saving methods achieve regret guarantees similar to their exact variants. Empirical evaluation confirms excellent performance, dominating standard approaches across memory versus accuracy tradeoffs.

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