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Stephanie Gil

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13 papers

2 author rows

ICRA Conference 2024 Conference Paper

Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map

Daniel Garces
Sushmita Bhattacharya
Dimitri P. Bertsekas
Stephanie Gil

In this paper, we focus on the autonomous multiagent taxi routing problem for a large urban environment where the location and number of future ride requests are unknown a-priori, but can be estimated by an empirical distribution. Recent theory has shown that a rollout algorithm with a stable base policy produces a near-optimal stable policy. In the routing setting, a policy is stable if its execution keeps the number of outstanding requests uniformly bounded over time. Although, rollout-based approaches are well-suited for learning cooperative multiagent policies with considerations for future demand, applying such methods to a large urban environment can be computationally expensive due to the large number of taxis required for stability. In this paper, we aim to address the computational bottleneck of multiagent rollout by proposing an approximate multiagent rollout-based two phase algorithm that reduces computational costs, while still achieving a stable near-optimal policy. Our approach partitions the graph into sectors based on the predicted demand and the maximum number of taxis that can run sequentially given the user’s computational resources. The algorithm then applies instantaneous assignment (IA) for re-balancing taxis across sectors and a sector-wide multiagent rollout algorithm that is executed in parallel for each sector. We provide two main theoretical results: 1) characterize the number of taxis m that is sufficient for IA to be stable; 2) derive a necessary condition on m to maintain stability for IA as time goes to infinity. Our numerical results show that our approach achieves stability for an m that satisfies the theoretical conditions. We also empirically demonstrate that our proposed two phase algorithm has equivalent performance to the one-at-a-time rollout over the entire map, but with significantly lower runtimes.

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IROS Conference 2024 Conference Paper

MULAN-WC: Multi-Robot Localization Uncertainty-aware Active NeRF with Wireless Coordination

Weiying Wang
Victor Cai
Stephanie Gil

This paper presents MULAN-WC, a novel multi-robot 3D reconstruction framework that leverages wireless signal-based coordination between robots and Neural Radiance Fields (NeRF). Our approach addresses key challenges in multi-robot 3D reconstruction, including inter-robot pose estimation, localization uncertainty quantification, and active best-next-view selection. We introduce a method for using wireless Angle-of-Arrival (AoA) and ranging measurements to estimate relative poses between robots, as well as quantifying and incorporating the uncertainty embedded in the wireless localization of these pose estimates into the NeRF training loss to mitigate the impact of inaccurate camera poses. Furthermore, we propose an active view selection approach that accounts for robot pose uncertainty when determining the best-next-views to improve the 3D reconstruction, enabling faster convergence through intelligent view selection. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our framework in theory and in practice. Leveraging wireless coordination and localization uncertainty-aware training, MULAN-WC can achieve high-quality 3D reconstruction that is close to applying the ground truth camera poses. Furthermore, the quantification of the information gain from a novel view enables consistent rendering quality improvement with incrementally captured images by commanding the robot to the novel view position. Our hardware experiments showcase the practicality of deploying MULAN-WC to real robotic systems.

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AAMAS Conference 2024 Conference Paper

Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare

Daniel Garces
Stephanie Gil

Large events such as conferences, concerts and sports games, often cause surges in demand for ride services that are not captured in average demand patterns, posing unique challenges for routing algorithms. We propose a learning framework for an autonomous fleet of taxis that leverages event data from the internet to predict demand surges and generate cooperative routing policies. We achieve this through a combination of two major components: (i) a demand prediction framework that uses textual event information in the form of events’ descriptions and reviews to predict eventdriven demand surges over street intersections, and (ii) a scalable multiagent reinforcement learning framework that leverages demand predictions and uses one-agent-at-a-time rollout, combined with limited sampling certainty equivalence, to learn intersectionlevel routing policies. For our experimental results we consider real NYC ride share data for the year 2022 and information for more than 2000 events across 300 unique venues in Manhattan. We test our approach with a fleet of 100 taxis on a map with 2235 street intersections. Our experimental results demonstrate that our method learns routing policies that reduce wait time overhead per serviced request by 25% to 75%, while picking up 1% to 4% more requests than other model-based RL frameworks and classical methods from operations research.

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ICRA Conference 2023 Conference Paper

Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots

Matthew Cavorsi
Orhan Eren Akgün
Michal Yemini
Andrea J. Goldsmith
Stephanie Gil

We develop a resilient binary hypothesis testing frame-work for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. Here, the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis simultaneously. We exploit special problem structure to show that this approach remains computationally tractable despite several unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions on a mock-up road network similar in spirit to Google Maps, subject to a Sybil attack. We extract the trust observations for each robot from actual communication signals which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30. 5% and 29% for the 2SA and the A-GLRT respectively.

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ICRA Conference 2023 Conference Paper

Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand

Daniel Garces
Sushmita Bhattacharya
Stephanie Gil
Dimitri P. Bertsekas

We derive a learning framework to generate routing/pickup policies for a fleet of autonomous vehicles tasked with servicing stochastically appearing requests on a city map. We focus on policies that 1) give rise to coordination amongst the vehicles, thereby reducing wait times for servicing requests, 2) are non-myopic, and consider a-priori potential future requests, 3) can adapt to changes in the underlying demand distribution. Specifically, we are interested in policies that are adaptive to fluctuations of actual demand conditions in urban environments, such as on-peak vs. off-peak hours. We achieve this through a combination of (i) an online play algorithm that improves the performance of an offline-trained policy, and (ii) an offline approximation scheme that allows for adapting to changes in the underlying demand model. In particular, we achieve adaptivity of our learned policy to different demand distributions by quantifying a region of validity using the q-valid radius of a Wasserstein Ambiguity Set. We propose a mechanism for switching the originally trained offline approximation when the current demand is outside the original validity region. In this case, we propose to use an offline architecture, trained on a historical demand model that is closer to the current demand in terms of Wasserstein distance. We learn routing and pickup policies over real taxicab requests in San Francisco with high variability between on-peak and off-peak hours, demonstrating the ability of our method to adapt to real fluctuation in demand distributions. Our numerical results demonstrate that our method outperforms alternative rollout-based reinforcement learning schemes, as well as other classical methods from operations research.

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ICRA Conference 2023 Conference Paper

Wi-Closure: Reliable and Efficient Search of Inter-robot Loop Closures Using Wireless Sensing

Weiying Wang
Anne Kemmeren
Daniel Son
Javier Alonso-Mora
Stephanie Gil

In this paper we propose a novel algorithm, Wi-Closure, to improve the computational efficiency and robustness of loop closure detection in multi-robot SLAM. Our approach decreases the computational overhead of classical approaches by pruning the search space of potential loop closures, prior to evaluation by a typical multi-robot SLAM pipeline. Wi-Closure achieves this by identifying candidates that are spatially close to each other measured via sensing over the wireless communication signal between robots, even when they are operating in non-line-of-sight or in remote areas of the environment from one another. We demonstrate the validity of our approach in simulation and in hardware experiments. Our results show that using Wi-closure greatly reduces computation time, by 54. 1% in simulation and 76. 8% in hardware experiments, compared with a multi-robot SLAM baseline. Importantly, this is achieved without sacrificing accuracy. Using Wi-closure reduces absolute trajectory estimation error by 98. 0% in simulation and 89. 2% in hardware experiments. This improvement is partly due to Wi-Closure's ability to avoid catastrophic optimization failure that typically occurs with classical approaches in challenging repetitive environments.

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ICRA Conference 2022 Conference Paper

Providing Local Resilience to Vulnerable Areas in Robotic Networks

Matthew Cavorsi
Stephanie Gil

We study how information flows through a multi-robot network in order to better understand how to provide resilience to malicious information. While the notion of global resilience is well studied, one way existing methods provide global resilience is by bringing robots closer together to improve the connectivity of the network. However, large changes in network structure can impede the team from performing other functions such as coverage, where the robots need to spread apart. Our goal is to mitigate the trade-off between resilience and network structure preservation by applying resilience locally in areas of the network where it is needed most. We introduce a metric, Influence, to identify vulnerable regions in the network requiring resilience. We design a control law targeting local resilience to the vulnerable areas by improving the connectivity of robots within these areas so that each robot has at least $2F+1$ vertex-disjoint communication paths between itself and the high influence robot in the vulnerable area. We demonstrate the performance of our local resilience controller in simulation and in hardware by applying it to a coverage problem and comparing our results with an existing global resilience strategy. For the specific hardware experiments, we show that our control provides local resilience to vulnerable areas in the network while only requiring 9. 90% and 15. 14% deviations from the desired team formation compared to the global strategy.

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IROS Conference 2022 Conference Paper

Toolbox Release: A WiFi-Based Relative Bearing Framework for Robotics

Ninad Jadhav
Weiying Wang
Diana Zhang
Swarun Kumar
Stephanie Gil

This paper presents the WiFi-Sensor-for-Robotics (WSR) open-source toolbox 1 1 1 Code: https://github.com/Harvard-REACT/WSR-Toolbox Dataset: https://github.com/Harvard-REACT/WSR-Toolbox-Dataset Demo: https://github.com/Harvard-REACT/WSR-Toolbox/wiki/Demo.It enables robots in a team to obtain relative bearing to each other, even in nonline-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an open-source toolbox. It is motivated by the lack of easily deployable solutions that use robots' local resources (e. g WiFi) for sensing in NLOS. This has implications for multi-robot mapping and rendezvous, ad-hoc robot networks, and security in multi-robot teams, amongst other applications. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings and for a multi-robot localization use case. Empirical results for hardware experiments show that the bearing estimation from our toolbox achieves accuracy with mean and standard deviation of 1. 13 degrees, 11. 07 degrees in LOS and 6. 04 degrees, 26. 4 degrees for NLOS, respectively, in an indoor office environment.

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ICRA Conference 2019 Conference Paper

Switching Topology for Resilient Consensus using Wi-Fi Signals

Thomas Wheeler
Ezhil Bharathi
Stephanie Gil

Securing multi-robot teams against malicious activity is crucial as these systems accelerate towards widespread societal integration. This emerging class of “physical networks” requires research into new methods of security that exploit their physical nature. This paper derives a theoretical framework for securing multi-agent consensus against the Sybil attack by using the physical properties of wireless transmissions. Our frame-work uses information extracted from the wireless channels to design a switching signal that stochastically excludes potentially untrustworthy transmissions from the consensus. Intuitively, this amounts to selectively ignoring incoming communications from untrustworthy agents, allowing for consensus to the true average to be recovered with high probability if initiated after a certain observation time T 0 that we derive. This work is different from previous work in that it allows for arbitrary malicious node values and is insensitive to the initial topology of the network so long as a connected topology over legitimate nodes in the network is feasible. We show that our algorithm will recover consensus and the true graph over the system of legitimate agents with an error rate that vanishes exponentially with time.

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ICRA Conference 2017 Conference Paper

Correcting robot mistakes in real time using EEG signals

Andres F. Salazar-Gomez
Joseph DelPreto
Stephanie Gil
Frank H. Guenther
Daniela Rus

Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink Robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction.

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ICRA Conference 2013 Conference Paper

K-robots clustering of moving sensors using coresets

Dan Feldman
Stephanie Gil
Ross A. Knepper
Brian J. Julian
Daniela Rus

We present an approach to position k servers (e. g. mobile robots) to provide a service to n independently moving clients; for example, in mobile ad-hoc networking applications where inter-agent distances need to be minimized, connectivity constraints exist between servers, and no a priori knowledge of the clients' motion can be assumed. Our primary contribution is an algorithm to compute and maintain a small representative set, called a kinematic coreset, of the n moving clients. We prove that, in any given moment, the maximum distance between the clients and any set of k servers is approximated by the coreset up to a factor of (1 ± ε), where ε > 0 is an arbitrarily small constant. We prove that both the size of our coreset and its update time is polynomial in k log(n)/ε. Although our optimization problem is NP-hard (i. e. , takes time exponential in the number of servers to solve), solving it on the small coreset instead of the original clients results in a tractable controller. The approach is validated in a small scale hardware experiment using robot servers and human clients, and in a large scale numerical simulation using thousands of clients.

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IROS Conference 2012 Conference Paper

Communication coverage for independently moving robots

Stephanie Gil
Dan Feldman
Daniela Rus

We consider the task of providing communication coverage to a group of sensing robots (sensors) moving independently to collect data. We provide communication via controlled placement of router vehicles that relay messages from any sensor to any other sensor in the system under the assumptions of 1) no cooperation from the sensors, and 2) only sensor-router or router-router communication over a maximum distance of R is reliable. We provide a formal framework and design provable exact and approximate (faster) algorithms for finding optimal router vehicle locations that are updated according to sensor movement. Using vehicle limitations, such as bounded control effort and maximum velocities of the sensors, our algorithm approximates areas that each router can reach while preserving connectivity and returns an expiration time window over which these positions are guaranteed to maintain communication of the entire system. The expiration time is compared against computation time required to update positions as a decision variable for choosing either the exact or approximate solution for maintaining connectivity with the sensors on-line.

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ICRA Conference 2010 Conference Paper

Optimizing communication in air-ground robot networks using decentralized control

Stephanie Gil
Mac Schwager
Brian J. Julian
Daniela Rus

We develop a distributed controller to position a team of aerial vehicles in a configuration that optimizes communication-link quality, to support a team of ground vehicles performing a collaborative task. We propose a gradient-based control approach where agents' positions locally minimize a physically motivated cost function. The contributions of this paper are threefold. We formulate of a cost function that incorporates a continuous, physical model of signal quality, SIR. We develop a non-smooth gradient-based controller that positions aerial vehicles to acheive optimized signal quality amongst all vehicles in the system. This controller is provably convergent while allowing for non-differentiability due to agents moving in or out of communication with one another. Lastly, we guarantee that given certain initial conditions or certain values of the control parameters, aerial vehicles will never disconnect the connectivity graph. We demonstrate our controller on hardware experiments using AscTec Hummingbird quadrotors and provide aggregate results over 10 trials. We also provide hardware-in-the-loop and MATALB simulation results, which demonstrate positioning of the aerial vehicles to minimize the cost function H and improve signal-quality amongst all communication links in the ground/air robot team.

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