Selected Publications

2020
Sensor information from wireless signals used for multi-robot PGO.
Sushmita Bhattacharya, Sahil Badyal, Thomas Wheeler, Stephanie Gil, and Dimitri Bertsekas. 1/23/2020. “Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems.” In RAL 1/23/2020. Abstract
—In this paper we consider infinite horizon discounted
dynamic programming problems with finite state and control
spaces, and partial state observations. We discuss an algorithm
that uses multistep lookahead, truncated rollout with a known
base policy, and a terminal cost function approximation. This
algorithm is also used for policy improvement in an approximate
policy iteration scheme, where successive policies are approximated by using a neural network classifier. A novel feature of
our approach is that it is well suited for distributed computation
through an extended belief space formulation and the use of a
partitioned architecture, which is trained with multiple neural
networks. We apply our methods in simulation to a class of
sequential repair problems where a robot inspects and repairs
a pipeline with potentially several rupture sites under partial
information about the state of the pipeline.
Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems
2019
Sensor information from wireless signals used for multi-robot PGO.
Weiying Wang, Ninad Jadhav, Paul Vohs, Nathan Hughes, Mark Mazumder, and Stephanie Gil. 12/29/2019. “Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi.” In International Symposium on Robotics Research (ISRR). Hanoi.Abstract

We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that addresses two important challenges for multi-robot SLAM: i) that of enabling information exchange “on-demand” via Active Rendezvous without using a map or the robot’s location, and ii) that of rejecting outlying measurements. Our key insight is to exploit relative position data present in the communication channel between robots to improve groundtruth accuracy of PGO. We develop an algorithmic and experimental framework for integrating Channel State Information (CSI) with multi-robot PGO; it is distributed, and applicable in low-lighting or featureless environments where traditional sensors often fail. We present extensive experimental results on actual robots and observe that using Active Rendezvous results in a 64% reduction in ground truth pose error and that using CSI observations to aid outlier rejection reduces ground truth pose error by 32%. These results show the potential of integrating communication as a novel sensor for SLAM.

Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi
Switching Topology for Resilient Consensus using Wi-Fi Signals
Thomas Wheeler, Ezhil Bharathi, and Stephanie Gil. 5/20/2019. “Switching Topology for Resilient Consensus using Wi-Fi Signals.” In IEEE International Conference on Robotics and Automation (ICRA).Abstract
Securing multi-robot teams against malicious ac- tivity is crucial as these systems accelerate towards widespread societal integration. This emerging class of “physical networks” requires new security methods 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 framework uses information extracted from the wireless channels to de- sign 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 after a certain observation time T0. This paper 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.
Switching Topology for Resilient Consensus using Wi-Fi Signals
Reinforcement Learning for POMDP: Rollout and Policy Iteration with Application to Sequential Repair
Dimitri P. Bertsekas, Stephanie Gil, Sushmita Bhattacharya, and Thomas Wheeler. 5/1/2019. “Reinforcement Learning for POMDP: Rollout and Policy Iteration with Application to Sequential Repair”.Abstract
We study rollout algorithms which combine limited lookahead and terminal cost function approximation in the context of POMDP. We demonstrate their effectiveness in the context of a sequential pipeline repair problem, which also arises in other contexts of search and rescue. We provide performance bounds and empirical validation of the methodology, in both cases of a single rollout iteration, and multiple iterations with intermediate policy space approximations.
Reinforcement Learning for POMDP: Rollout and Policy Iteration with Application to Sequential Repair
Resilient Multi-Agent Consensus Using Wi-Fi Signals
Stephanie Gil, Cenk Baykal, and Daniela Rus. 1/1/2019. “Resilient Multi-Agent Consensus Using Wi-Fi Signals .” In IEEE Control Systems Letters (L-CSS) 2018.Abstract
Consensus is an important capability at the heart of many multi-agent systems. Unfortunately the ability to reach consensus can be easily disrupted by the presence of an adversarial agent that spawns or spoofs malicious nodes in the network in order to gain a disproportionate influence on the converged value of the system as a whole. In this letter, we present a light-weight approach for spoof-resiliency with provable guarantees that solely utilizes information from wireless signals. Unlike prior approaches, our method requires no additional protocol or data storage beyond signals that are already present in the network. We establish an analytical, probabilistic bound on the influence of spoofed nodes in the system on the converged consensus value. We present results of our Wi-Fi based resilient consensus algorithm and demonstrate its effectiveness for different consensus problems such as flocking and rendezvous.
Resilient Multi-Agent Consensus Using Wi-Fi Signals
2018
Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection
Joseph DelPreto, Andres F. Salazar-Gomez, Stephanie Gil, Ramin M. Hasani, Frank H. Guenther, and Daniela Rus. 6/26/2018. “Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection.” In RSS 2018: Robotics: Science and Systems. RSS 6/26/2018. Abstract
Control of robots in safety-critical tasks and situations where costly errors may occur is paramount for realizing the vision of pervasive human-robot collaborations. For these cases, the ability to use human cognition in the loop can be key for recuperating safe robot operation. This paper combines two streams of human biosignals, electrical muscle and brain activity via EMG and EEG, respectively, to achieve fast and accurate human intervention in a supervisory control task. In particular, this paper presents an end-to-end system for continuous rollingwindow classification of gestures that allows the human to actively correct the robot on demand, discrete classification of Error-Related Potential signals (unconsciously produced by the human supervisor’s brain when observing a robot error), and a framework that integrates these two classification streams for fast and effective human intervention. The system also allows “plug-and-play” operation, demonstrating accurate performance even with new users whose biosignals have not been used for training the classifiers. The resulting hybrid control system for safety-critical situations is evaluated with 7 untrained human subjects in a supervisory control scenario where an autonomous robot performs a multi-target selection task.
Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection
2017
Correcting robot mistakes in real time using EEG signals
A.F. Salazar-Gomez, J DelPreto, S. Gil, F.H. Guenther, and D. Rus. 5/29/2017. “Correcting robot mistakes in real time using EEG signals.” In IEEE International Conference on Robotics and Automation (ICRA). Singapore.Abstract
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.
Correcting robot mistakes in real time using EEG signals
Guaranteeing spoof-resilient multi-robot networks
Stephanie Gil, Swarun Kumar, Mark Mazumder, Dina Katabi, and Daniela Rus. 2/28/2017. “Guaranteeing spoof-resilient multi-robot networks .” In .Abstract
Multi-robot networks use wireless communication to provide wide-ranging services such as aerial surveillance and unmanned delivery. However, effective coordination between multiple robots requires trust, making them particularly vulnerable to cyber-attacks. Specifically, such networks can be gravely disrupted by the Sybil attack, where even a single malicious robot can spoof a large number of fake clients. This paper proposes a new solution to defend against the Sybil attack, without requiring expensive cryptographic key-distribution. Our core contribution is a novel algorithm implemented on commercial Wi-Fi radios that can “sense” spoofers using the physics of wireless signals. We derive theoretical guarantees on how this algorithm bounds the impact of the Sybil Attack on a broad class of multi-robot problems, including locational coverage and unmanned delivery. We experimentally validate our claims using a team of AscTec quadrotor servers and iRobot Create ground clients, and demonstrate spoofer detection rates over 96%.
Guaranteeing spoof-resilient multi-robot networks
2015
Guaranteeing Spoof-Resilient Multi-Robot Networks
S. Gil, S. Kumar, D. Katabi, and D. Rus. 7/13/2015. “Guaranteeing Spoof-Resilient Multi-Robot Networks.” In Robotics Science and Systems (RSS). Rome, Italy.Abstract
Multi-robot networks use wireless communication to provide wide-ranging services such as aerial surveillance and unmanned delivery. However, effective coordination between multiple robots requires trust, making them particularly vulnerable to cyber-attacks. Specifically, such networks can be gravely disrupted by the Sybil attack, where even a single malicious robot can spoof a large number of fake clients. This paper proposes a new solution to defend against the Sybil attack, without requiring expensive cryptographic key-distribution. Our core contribution is a novel algorithm implemented on commercial Wi-Fi radios that can “sense” spoofers using the physics of wireless signals. We derive theoretical guarantees on how this algorithm bounds the impact of the Sybil Attack on a broad class of multi-robot problems, including locational coverage and unmanned delivery. We experimentally validate our claims using a team of AscTec quadrotor servers and iRobot Create ground clients, and demonstrate spoofer detection rates over 96%.
Guaranteeing spoof-resilient multi-robot networks
Adaptive Communication in Multi-Robot Teams using Directionality of Signal Strength
S. Gil, S. Kumar, D. Katabi, and D. Rus. 3/18/2015. “Adaptive Communication in Multi-Robot Teams using Directionality of Signal Strength.” International Journal on Robotics Research, 34, 7, Pp. 946-968.Abstract
We consider the problem of satisfying communication demands in a multi-agent system where several robots cooperate on a task and a fixed subset of the agents act as mobile routers. Our goal is to position the team of robotic routers to provide communication coverage to the remaining client robots. We allow for dynamic environments and variable client demands, thus necessitating an adaptive solution. We present an innovative method that calculates a mapping between a robot’s current position and the signal strength that it receives along each spatial direction, for its wireless links to every other robot. We show that this information can be used to design a simple positional controller that retains a quadratic structure, while adapting to wireless signals in real-world environments. Notably, our approach does not necessitate stochastic sampling along directions that are counter-productive to the overall coordination goal, nor does it require exact client positions, or a known map of the environment.
Adaptive Communication in Multi-Robot Teams using Directionality of Signal Strength
2014
Accurate Indoor Localization with Zero Startup Cost
S. Kumar, S. Gil, D. Rus, and D. Katabi. 9/7/2014. “Accurate Indoor Localization with Zero Startup Cost.” In ACM Conference on Mobile Computing and Networking (MobiCom).Abstract
Recent years have seen the advent of new RF-localization systems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption. We present Ubicarse, an accurate indoor localization system for commodity mobile devices, with no specialized infrastructure or fingerprinting. Ubicarse enables handheld devices to emulate large antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Past work on SAR requires measuring mechanically controlled device movement with millimeter precision, far beyond what commercial accelerometers can provide. In contrast, Ubicarse’s core contribution is the ability to perform SAR on handheld devices twisted by their users along unknown paths. Ubicarse is not limited to localizing RF devices; it combines RF localization with stereo-vision algorithms to localize common objects with no RF source attached to them. We implement Ubicarse on a HP SplitX2 tablet and empirically demonstrate a median error of 39 cm in 3-D device localization and 17 cm in object geotagging in complex indoor settings.
Accurate Indoor Localization with Zero Startup Cost
2013
Adaptive Communication in Multi-Robot Systems Using Directionality of Signal Strength
S. Gil, S. Kumar, D. Katabi, and D. Rus. 12/16/2013. “Adaptive Communication in Multi-Robot Systems Using Directionality of Signal Strength.” In International Symposium on Robotics Research (ISRR). Singapore.Abstract
We consider the problem of satisfying communication demands in a multi-agent system where several robots cooperate on a task and a fixed subset of the agents act as mobile routers. Our goal is to position the team of robotic routers to provide communication coverage to the remaining client robots. We allow for dynamic environments and variable client demands, thus necessitating an adaptive solution. We present an innovative method that calculates a mapping between a robot’s current position and the signal strength that it receives along each spatial direction, for its wireless links to every other robot. We show that this information can be used to design a simple positional controller that retains a quadratic structure, while adapting to wireless signals in real-world environments. Notably, our approach does not necessitate stochastic sampling along directions that are counter-productive to the overall coordination goal, nor does it require exact client positions, or a known map of the environment.
Adaptive Communication in Multi-Robot Teams using Directionality of Signal Strength
Communication coverage for independently moving robots
S. Gil, D. Feldman, and D. Rus. 11/3/2013. “Communication coverage for independently moving robots.” In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Tokyo, Japan.Abstract
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 routerrouter 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.
Communication coverage for independently moving robots
K-robots clustering of moving sensors using clustering
D. Feldman, S. Gil, B. Julian, R. Knepper, and D. Rus. 5/6/2013. “K-robots clustering of moving sensors using clustering .” In IEEE International Conference on Robotics and Automation (ICRA). Sing.Abstract
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.
K-robots clustering of moving sensors using clustering
2012
Carspeak: A content-centric network for autonomous driving
S. Kumar, L. Shi, N. Ahmed, S. Gil, D. Katabi, and D. Rus. 8/14/2012. “Carspeak: A content-centric network for autonomous driving .” In ACM SIGCOMM. Helsinki, Finland.Abstract
This paper introduces CarSpeak, a communication system for autonomous driving. CarSpeak enables a car to query and access sensory information captured by other cars in a manner similar to how it accesses information from its local sensors. CarSpeak adopts a content-centric approach where information objects – i.e., regions along the road – are first class citizens. It names and accesses road regions using a multi-resolution system, which allows it to scale the amount of transmitted data with the available bandwidth. CarSpeak also changes the MAC protocol so that, instead of having nodes contend for the medium, contention is between road regions, and the medium share assigned to any region depends on the number of cars interested in that region. CarSpeak is implemented in a state-of-the-art autonomous driving system and tested on indoor and outdoor hardware testbeds including an autonomous golf car and 10 iRobot Create robots. In comparison with a baseline that directly uses 802.11, CarSpeak reduces the time for navigating around obstacles by 2.4×, and reduces the probability of a collision due to limited visibility by 14×
Carspeak: A content-centric network for autonomous driving
2011
Decentralized control for optimizing communication with infeasible regions
S. Gil, S. Prentice, N. Roy, and D. Rus. 12/9/2011. “Decentralized control for optimizing communication with infeasible regions.” In International Symposium on Robotics Research (ISRR). Flagstaff, Arizona.Abstract
In this paper we present a decentralized gradient-based controller that optimizes communication between mobile aerial vehicles and stationary ground sensor vehicles in an environment with infeasible regions. The formulation of our problem as a MIQP is easily implementable, and we show that the addition of a scaling matrix can improve the range of attainable converged solutions by influencing trajectories to move around infeasible regions. We demonstrate the robustness of the controller in 3D simulation with agent failure, and in 10 trials of a multi-agent hardware experiment with quadrotors and ground sensors in an indoor environment. Lastly, we provide analytical guarantees that our controller strictly minimizes a nonconvex cost along agent trajectories, a desirable property for general multi-agent coordination tasks.
Decentralized control for optimizing communication with infeasible regions
2009
Beyond local optimality: An improved approach to hybrid model learning
S. Gil and B. Williams. 12/16/2009. “Beyond local optimality: An improved approach to hybrid model learning.” In 48th IEEE Conference on Decision and Control. Shanghai.Abstract
Local convergence is a limitation of many optimization approaches for multimodal functions. For hybrid model learning, this can mean a compromise in accuracy. We develop an approach for learning the model parameters of hybrid discrete-continuous systems that avoids getting stuck in locally optimal solutions. We present an algorithm that implements this approach that 1) iteratively learns the locations and shapes of explored local maxima of the likelihood function, and 2) focuses the search away from these areas of the solution space, toward undiscovered maxima that are a priori likely to be optimal solutions. We evaluate the algorithm on autonomous underwater vehicle (AUV) data. Our aggregate results show reduction in distance to the global maximum by 16% in 10 iterations, averaged over 100 trials, and iterative increase in log-likelihood value of learned model parameters, demonstrating the ability of the algorithm to guide the search toward increasingly better optima of the likelihood function, avoiding local convergence.
Beyond local optimality: An improved approach to hybrid model learning
2007
Model learning for switching linear systems with autonomous mode transitions
L. Blackmore, S. Gil, S. Chung, and B. Williams. 12/12/2007. “Model learning for switching linear systems with autonomous mode transitions.” In 46th IEEE Conference on Decision and Control. New Orleans, LA, USA.Abstract
We present a novel method for model learning in hybrid discrete-continuous systems. The approach uses approximate expectation-maximization to learn the maximum- likelihood parameters of a switching linear system. The approach extends previous work by 1) considering autonomous mode transitions, where the discrete transitions are conditioned on the continuous state, and 2) learning the effects of control inputs on the system. We evaluate the approach in simulation.
Model learning for switching linear systems with autonomous mode transitions