Author name cluster

Ethan Stump

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

2 author rows

AAMAS Conference 2022 Conference Paper

The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts

Amanda Prorok
Jan Blumenkamp
Qingbiao Li
Ryan Kortvelesy
Zhe Liu
Ethan Stump

Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of learning-based methods in multi-robot planning holds great promise as it enables us to offload the online computational burden of expensive centralized, yet optimal solvers, to an offline learning procedure. The hope is that by training a policy to copy an optimal pattern generated by a small-scale (centralized) system, we can transfer that policy to much larger, decentralized systems while maintaining near-optimal performance. Yet, a number of issues impede us from leveraging this idea to its full potential. This blue-sky paper elaborates some of the key challenges that remain.

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ICRA Conference 2020 Conference Paper

Test Your SLAM! The SubT-Tunnel dataset and metric for mapping

John G. Rogers
Jason M. Gregory
Jonathan Fink
Ethan Stump

This paper presents an approach and introduces new open-source tools that can be used to evaluate robotic mapping algorithms. Also described is an extensive subterranean mine rescue dataset based upon the DARPA Subterranean (SubT) challenge including professionally surveyed ground truth. Finally, some commonly available approaches are evaluated using this metric.

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JMLR Journal 2019 Journal Article

Parsimonious Online Learning with Kernels via Sparse Projections in Function Space

Alec Koppel
Garrett Warnell
Ethan Stump
Alejandro Ribeiro

Despite their attractiveness, popular perception is that techniques for nonparametric function approximation do not scale to streaming data due to an intractable growth in the amount of storage they require. To solve this problem in a memory-affordable way, we propose an online technique based on functional stochastic gradient descent in tandem with supervised sparsification based on greedy function subspace projections. The method, called parsimonious online learning with kernels (POLK), provides a controllable tradeoff between its solution accuracy and the amount of memory it requires. We derive conditions under which the generated function sequence converges almost surely to the optimal function, and we establish that the memory requirement remains finite. We evaluate POLK for kernel multi-class logistic regression and kernel hinge-loss classification on three canonical data sets: a synthetic Gaussian mixture model, the MNIST hand-written digits, and the Brodatz texture database. On all three tasks, we observe a favorable trade-off of objective function evaluation, classification performance, and complexity of the nonparametric regressor extracted by the proposed method. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

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IROS Conference 2018 Conference Paper

Composable Learning with Sparse Kernel Representations

Ekaterina I. Tolstaya
Ethan Stump
Alec Koppel
Alejandro Ribeiro

We present a reinforcement learning algorithm for learning sparse non-parametric controllers in a Reproducing Kernel Hilbert Space. We improve the sample complexity of this approach by imposing a structure of the state-action function through a normalized advantage function (NAF). This representation of the policy enables efficiently composing multiple learned models without additional training samples or interaction with the environment. We demonstrate the performance of this algorithm on learning obstacle-avoidance policies in multiple simulations of a robot equipped with a laser scanner while navigating in a 2D environment. We apply the composition operation to various policy combinations and test them to show that the composed policies retain the performance of their components. We also transfer the composed policy directly to a physical platform operating in an arena with obstacles in order to demonstrate a degree of generalization.

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IROS Conference 2016 Conference Paper

Online learning for characterizing unknown environments in ground robotic vehicle models

Alec Koppel
Jonathan Fink
Garrett Warnell
Ethan Stump
Alejandro Ribeiro

In pursuit of increasing the operational tempo of a ground robotics platform in unknown domains, we consider the problem of predicting the distribution of structural state-estimation error due to poorly-modeled platform dynamics as well as environmental effects. Such predictions are a critical component of any modern control approach that utilizes uncertainty information to provide robustness in control design. We use an online learning algorithm based on matrix factorization techniques to fit a statistical model of error that provides enough expressive power to enable prediction directly from motion control signals and low-level visual features. Moreover, we empirically demonstrate that this technique compares favorably to predictors that do not incorporate this information.

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IROS Conference 2015 Conference Paper

D4L: Decentralized dynamic discriminative dictionary learning

Alec Koppel
Garrett Warnell
Ethan Stump
Alejandro Ribeiro

We consider discriminative dictionary learning in a distributed online setting, where a team of networked robots aims to jointly learn both a common basis of the feature space and a classifier over this basis from sequentially observed signals. We formulate this problem as a distributed stochastic program with a non-convex objective and present a block variant of the Arrow-Hurwicz saddle point algorithm to solve it. Only neighboring nodes in the communications network need to exchange information, and we penalize the discrepency between the individual feature basis and classifiers using Lagrange multipliers. The application we consider is for a team of robots to collaboratively recognize objects of interest in dynamic environments. As a preliminary performance benchmark, we consider the problem of learning a texture classifier across a network of robots moving around an urban setting where separate training examples are sequentially observed at each robot. Results are shown for both a standard texture dataset and a new dataset from an urban training facility, and we compare the performance of the standard centralized construction to the new distributed algorithm for the case when distinct samples from all classes are seen by the robots. These experiments yield comparable performance between the decentralized and the centralized cases, demonstrating the proposed method's practical utility.

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IROS Conference 2014 Conference Paper

Experimental analysis of models for trajectory generation on tracked vehicles

Jonathan Fink
Ethan Stump

We begin to bridge the gap between high-level motion planning and execution by adopting models to abstract the complicated skid-steer vehicle dynamics and evaluating their suitability as motion predictors for a feed-forward control framework. We consider three kinematic motion models and a drivetrain model in experiments on two surface types with a small tracked vehicle. We perform statistical analysis of the predictive accuracy of these models when used to create optimal open-loop plans for a set of canonical maneuvers and discuss the applicability of these models for a closed-loop control framework.

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IROS Conference 2011 Conference Paper

Persistent surveillance with a team of MAVs

Nathan Michael
Ethan Stump
Kartik Mohta

In this paper, we focus on the detailing of a system architecture capable of addressing the problem of persistent surveillance with a team of autonomous micro-aerial vehicles (MAVs). We detail the problem of interest, discuss system requirements, and provide an overview of our approach. The remainder of the paper is dedicated to the system design and evaluation on a team of quadrotors in simulation and experiments.

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ICRA Conference 2011 Conference Paper

Visibility-based deployment of robot formations for communication maintenance

Ethan Stump
Nathan Michael
Vijay Kumar 0001
Volkan Isler

We consider the problem of deploying robots in formations that ensure network connectivity between a fixed base station and a set of independent agents wandering in the environment. We adopt a communications model that requires line-of-sight and then solve for robot placements by finding mutually-visible configurations in a polygonal decomposition of the environment map. Both the static deployment case and the case of finding deployments that minimize total robot movement are considered. We provide algorithms for the moving agent case, consider their performance on various discretizations for a range of problem sizes, and discuss our experimental implementation of the presented ideas.

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IROS Conference 2010 Conference Paper

Effects of increasing autonomy on tele-operation performance

Barry J. O'Brien
Ethan Stump
Cynthia S. Pierce

Tele-operation of robotic platforms has long suffered from the inability of the operator to effectively perform ancillary tasks while controlling the robot. Because of the focus required to perform tele-operation, the operator is limited in their ability to use the robot to improve their situational awareness, with tele-operation often becoming a detriment to their task rather than an enhancement. We present experimental results on the use of a tele-operated robotic system modified to include layered obstacle detection and avoidance routines and an open space planner. These algorithms help the operator shift their focus towards high-level tasks instead of low-level navigation. While experimental data shows that the increase in autonomy did not lead to a reduction in task completion time, obstacle collisions were reduced and has led to further investigation of cognitive load reduction during operation.

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ICRA Conference 2008 Conference Paper

Connectivity management in mobile robot teams

Ethan Stump
Ali Jadbabaie
Vijay Kumar 0001

We develop a framework for controlling a team of robots to maintain and improve a communication bridge between a stationary robot and an independently exploring robot in a walled environment. We make use of two metrics for characterizing the communication: the Fiedler value of the weighted Laplacian describing the communication interactions of all the robots in the system, and the k-connectivity matrix that expresses which robots can interact through k or less intermediary robots. At each step, we move in such a way as to improve the Fiedler value as much as possible while keeping the number of intermediary robots between the two robots of interest below a desired value. We demonstrate the use of this framework in a scenario where the hop-count constraint cannot be satisfied, but show that communication quality is maintained anyways.

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