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Roberto Tron

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19 papers
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19

AAMAS Conference 2025 Conference Paper

Adaptive Bi-Level Multi-Robot Task Allocation and Learning under Uncertainty with Temporal Logic Constraints

  • Xiaoshan Lin
  • Roberto Tron

This work addresses the problem of multi-robot coordination under unknown robot transition models, ensuring that tasks specified by Time Window Temporal Logic are satisfied with user-defined probability thresholds. We present a bi-level framework that integrates (i) high-level task allocation, where tasks are assigned based on the robots’ estimated task completion probabilities and expected rewards, and (ii) low-level distributed policy learning and execution, where robots independently optimize auxiliary rewards while fulfilling their assigned tasks. To handle uncertainty in robot dynamics, our approach leverages data collected during task execution to iteratively refine the expected task completion probabilities and rewards, enabling adaptive task allocation without explicit robot transition models. We theoretically validate the proposed algorithm by showing that it ensures tasks are completed at the desired probability thresholds with high confidence. Finally, we demonstrate the efficacy of our framework through comprehensive simulations.

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ICRA Conference 2025 Conference Paper

BoxMap: Efficient Structural Mapping and Navigation

  • Zili Wang
  • Christopher Allum
  • Sean B. Andersson
  • Roberto Tron

While humans can successfully navigate using abstractions, ignoring details that are irrelevant to the task at hand, most of the existing approaches in robotics require detailed environment representations which consume a significant amount of sensing, computing, and storage; these issues become particularly important in resource-constrained settings with limited power budgets. Deep learning methods can learn from prior experience to abstract knowledge from novel environments, and use it to more efficiently execute tasks such as frontier exploration, object search, or scene understanding. We propose BoxMap, a Detection-Transformer-based architecture that takes advantage of the structure of the sensed partial environment to update a topological graph of the environment as a set of semantic entities (rooms and doors) and their relations (connectivity). The predictions from low-level measurements can be leveraged to achieve high-level goals with lower computational costs than methods based on detailed representations. As an example application, we consider a robot equipped with a 2-D laser scanner tasked with exploring a residential building. Our BoxMap representation scales quadratically with the number of rooms (with a small constant), resulting in significant savings over a full geometric map. Moreover, our high-level topological representation results in 30. 9 % shorter trajectories in the exploration task with respect to a standard method. Code is available at: bit. ly/3F6w2Yl.

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AAMAS Conference 2023 Conference Paper

Byzantine Resilience at Swarm Scale: A Decentralized Blocklist Protocol from Inter-robot Accusations

  • Kacper Wardega
  • Max von Hippel
  • Roberto Tron
  • Cristina Nita-Rotaru
  • Wenchao Li

The Weighted-Mean Subsequence Reduced (W-MSR) algorithm, the state-of-the-art method for Byzantine-resilient design of decentralized multi-robot systems, is based on discarding outliers received over Linear Consensus Protocol (LCP). Although W-MSR provides theoretical guarantees relating network connectivity to the convergence of the underlying consensus, W-MSR comes with several limitations: the number of Byzantine robots, 𝐹, to tolerate should be known a priori, each robot needs to maintain 2𝐹 + 1 neighbors, 𝐹 + 1 robots must independently make local measurements of the consensus property in order for the swarm’s decision to change, and W-MSR is specific to LCP and does not generalize to applications not implemented over LCP. In this work, we propose a Decentralized Blocklist Protocol (DBP) based on inter-robot accusations. Accusations are made on the basis of locally-made observations of misbehavior, and once shared by cooperative robots across the network are used as input to a graph matching algorithm that computes a blocklist. DBP generalizes to applications not implemented via LCP, is adaptive to the number of Byzantine robots, and allows for fast information propagation through the multirobot system while simultaneously reducing the required network connectivity relative to W-MSR. On LCP-type applications, DBP reduces the worst-case connectivity requirement of W-MSR from (2𝐹 + 1)-connected to (𝐹 + 1)-connected and the minimum number of cooperative observers required to propagate new information from 𝐹 + 1 to just 1 observer. We demonstrate that our approach to Byzantine resilience scales to hundreds of robots on target tracking, time synchronization, and localization case studies.

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IROS Conference 2023 Conference Paper

Do More with Less: Single-Model, Multi-Goal Architectures for Resource-Constrained Robots

  • Zili Wang
  • Drew Threatt
  • Sean B. Andersson
  • Roberto Tron

Deep learning methods are widely used in robotic applications. By learning from prior experience, the robot can abstract knowledge of the environment, and use this knowledge to accomplish different goals, such as object search, frontier exploration, or scene understanding, with a smaller amount of resources than might be needed without that knowledge. Most existing methods typically require a significant amount of sensing, which in turn has significant costs in terms of power consumption for acquisition and processing, and typically focus on models that are tuned for each specific goal, leading to the need to train, store and run each one separately. These issues are particularly important in a resource-constrained setting, such as with small-scale robots or during long-duration missions. We propose a single, multi-task deep learning architecture that takes advantage of the structure of the partial environment to predict different abstractions of the environment (thus reducing the need for rich sensing), and to leverage these predictions to simultaneously achieve different high-level goals (thus sharing computation between goals). As an example application of the proposed architecture, we consider the specific example of a robot equipped with a 2-D laser scanner and an object detector, tasked with searching for an object (such as an exit) in a residential building while constructing a topological map that can be used for future missions. The prior knowledge of the environment is encoded using a U-Net deep network architecture. In this context, our work leads to an object search algorithm that is complete, and that outperforms a more traditional frontier-based approach. The topological map we produce uses scene trees to qualitatively represent the environment as a graph at a fraction of the cost of existing SLAM-based solutions. Our results demonstrate that it is possible to extract multi-task semantic information that is useful for navigation and mapping directly from bare-bone, non-semantic measurements.

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AAMAS Conference 2023 Conference Paper

HoLA Robots: Mitigating Plan-Deviation Attacks in Multi-Robot Systems with Co-Observations and Horizon-Limiting Announcements

  • Kacper Wardega
  • Max von Hippel
  • Roberto Tron
  • Cristina Nita-Rotaru
  • Wenchao Li

In centralized multi-robot systems, a central entity (CE) checks that robots follow their assigned motion plans by comparing their expected location to the location they self-report. We show that this self-reporting monitoring mechanism is vulnerable to plandeviation attacks where compromised robots don’t follow their assigned plans while trying to conceal their movement by misreporting their location. We propose a two-pronged mitigation for plan-deviation attacks: (1) an attack detection technique leveraging both the robots’ local sensing capabilities to report observations of other robots and co-observation schedules generated by the CE, and (2) a prevention technique where the CE issues horizon-limiting announcements to the robots, reducing their instantaneous knowledge of forward lookahead steps in the global motion plan. On a large-scale automated warehouse benchmark, we show that our solution enables attack prevention guarantees from a stealthy attacker that has compromised multiple robots.

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IROS Conference 2023 Conference Paper

Reactive and Safe Co-Navigation with Haptic Guidance

  • Mela C. Coffey
  • Dawei Zhang 0005
  • Roberto Tron
  • Alyssa Pierson

We propose a co-navigation algorithm that enables a human and a robot to work together to navigate to a common goal. In this system, the human is responsible for making high-level steering decisions, and the robot, in turn, provides haptic feedback for collision avoidance and path suggestions while reacting to changes in the environment. Our algorithm uses optimized Rapidly-exploring Random Trees (RRT*) to generate paths to lead the user to the goal, via an attractive force feedback computed using a Control Lyapunov Function (CLF). We simultaneously ensure collision avoidance where necessary using a Control Barrier Function (CBF). We demonstrate our approach using simulations with a virtual pilot, and hardware experiments with a human pilot. Our results show that combining RRT* and CBFs is a promising tool for enabling collaborative human-robot navigation.

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IROS Conference 2022 Conference Paper

Koopman pose predictions for temporally consistent human walking estimations

  • Marc Mitjans
  • David M. Levine
  • Louis N. Awad
  • Roberto Tron

We tackle the problem of tracking the human lower body as an initial step toward an automatic motion assessment system for clinical mobility evaluation, using a multimodal system that combines Inertial Measurement Unit (IMU) data, RGB images, and point cloud depth measurements. This system applies the factor graph representation to an optimization problem that provides 3-D skeleton joint estimations. In this paper, we focus on improving the temporal consistency of the estimated human trajectories to greatly extend the range of operability of the depth sensor. More specifically, we introduce a new factor graph factor based on Koopman theory that embeds the nonlinear dynamics of several lower-limb movement activities. This factor performs a two-step process: first, a custom activity recognition module based on spatial temporal graph convolutional networks recognizes the walking activity; then, a Koopman pose prediction of the subsequent skeleton is used as an a priori estimation to drive the optimization problem toward more consistent results. We tested the performance of this module a dataset composed of multiple clinical lower-limb mobility tests, and we show that our approach reduces outliers on the skeleton form by almost 1 m, while preserving natural walking trajectories at depths up to more than 10 m.

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ICRA Conference 2021 Conference Paper

Haptic Feedback Improves Human-Robot Agreement and User Satisfaction in Shared-Autonomy Teleoperation

  • Dawei Zhang 0005
  • Roberto Tron
  • Rebecca P. Khurshid

Shared autonomy teleoperation can guarantee safety, but does so by reducing the human operator’s control authority, which can lead to reduced levels of human-robot agreement and user satisfaction. This paper presents a novel haptic shared autonomy teleoperation paradigm that uses haptic feedback to inform the user about the inner state of a shared autonomy paradigm, while still guaranteeing safety. This differs from haptic shared control, which uses haptic feedback to inform the user’s actions, but gives the human operator full control over the robot’s actions. We conducted a user study in which twelve users flew a simulated UAV in a search-and-rescue task with no assistance or assistance provided by haptic shared control, shared autonomy, or haptic shared autonomy. All assistive teleoperation methods use control barrier functions to find a control command that is both safe and as close as possible to the human-generated control command. For assistive teleoperation conditions with haptic feedback, we apply a force to the user that is proportional to the difference between the human-generated control and the safe control. We find that haptic shared autonomy improves the user’s task performance and satisfaction. We also find that haptic feedback in assistive teleoperation can improve the user’s situational awareness. Finally, results show that adding haptic feedback to shared-autonomy teleoperation can improve human-robot agreement.

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IROS Conference 2021 Conference Paper

Robust Sample-Based Output-Feedback Path Planning

  • Mahroo Bahreinian
  • Marc Mitjans
  • Roberto Tron

We propose a novel approach for sampling-based and control-based motion planning. We combine a representation of the environment obtained via a modified version of optimal Rapidly-exploring Random Trees (RRT * ), with landmark-based output-feedback controllers obtained via Control Lyapunov Functions, Control Barrier Functions, and robust Linear Programming. Our solution inherits many benefits of RRT * -like algorithms, such as the ability to implicitly handle arbitrarily complex obstacles. Additionally, it extends planning beyond the discrete nominal paths, as feedback controllers can correct deviations from such paths, and are robust to discrepancies between the planning and real environment maps. We test our algorithms first in simulations and then in experiments, evaluating the robustness of the approach to practical conditions, such as deformations of the environment, mismatches in the dynamical model of the robot, and measurements acquired with a camera with a limited field of view.

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ICRA Conference 2021 Conference Paper

Sensing via Collisions: a Smart Cage for Quadrotors with Applications to Self-Localization

  • Cheng Liu
  • Roberto Tron

Applications of micro unmanned aerial vehicles (UAVs) are gradually expanding into complex urban and natural environments. Despite noticeable progress, flying robots in obstacle-rich environments is still challenging. On-board processing for detecting and avoiding obstacles is possible, but at a significant computational expense, and with significant limitations (e. g. , for obstacles with small cross sections, such as wires). A low-cost alternative is to mitigate physical contacts through a cage or other similar protective devices. In this paper, we propose to transform these passive protective devices into functional sensors: we introduce a suspended rim combined with a central base measuring the relative displacement of the rim; we provide a full mechanical design, and derive solutions to the inverse kinematics for recovering the collision direction in real time. As a proof of concept, we show the benefits of this novel form of sensing by embedding it in a traditional particle filter for self-localization in a known environment; our experiments show that localization is possible with a minimal sacrifice in payload capacity.

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IROS Conference 2021 Conference Paper

Stable Haptic Teleoperation of UAVs via Small L2 Gain and Control Barrier Functions

  • Dawei Zhang 0005
  • Roberto Tron

We present a novel haptic teleoperation approach that considers not only the safety but also the stability of a teleoperation system. Specifically, we build upon previous work on haptic shared control, which generates a reference haptic feedback that helps the human operator to safely navigate the robot but without taking away their control authority. Crucially, in this approach the force rendered to the user is not directly reflected in the motion of the robot (which is still directly controlled by the user); however, previous work in the area neglected to consider the possible instabilities in feedback loop generated by a user that over-responds to the haptic force. In this paper we introduce a differential constraint on the rendered force that makes the system finite-gain ${{\mathcal{L}}_2}$ stable; the constraint results in a Quadratically Constrained Quadratic Program (QCQP), for which we provide a closed-form solution. Our constraint is related to, but less restrictive than, the typical passivity constraint used in previous literature. We conducted an experimental simulation in which a human operator flies a UAV near an obstacle to evaluate the proposed method.

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ICRA Conference 2021 Conference Paper

Visual-Inertial Filtering for Human Walking Quantification

  • Marc Mitjans
  • Michail Theofanidis
  • Ashley N. Collimore
  • Madelaine L. Disney
  • David M. Levine
  • Louis N. Awad
  • Roberto Tron

We propose a novel system to track human lower-body motion as part of a larger movement assessment system for clinical evaluation. Our system combines multiple wearable Inertial Measurement Unit (IMU) sensors and a single external RGB-D camera. We use a factor graph with a Sliding Window Filter (SWF) formulation that merges 2-D joint data extracted from the RGB images via a Deep Neural Network, raw depth information, raw IMU gyroscope readings, and estimated foot contacts extracted from IMU gyroscope and accelerometer data. For the system, we use an articulated model of human body motion based on differential manifolds. We compare the results of our system against a gold-standard motion capture system and a vision-only alternative. Our proposed system qualitatively presents smoother 3D joint trajectories when compared to noisy depth data, allowing for more realistic gait estimations. At the same time, with respect to the vision-only baseline, it improves the median of the joint trajectories by around 2cm, while considerably reducing outliers by up to 0. 6m.

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IROS Conference 2020 Conference Paper

Multi-Agent Path Planning Under Observation Schedule Constraints

  • Ziqi Yang
  • Roberto Tron

We consider the problem of enhanced security of multi-robot systems to prevent cyber-attackers from taking control of one or more robots in the group. We build upon a recently proposed solution that utilizes the physical measurement capabilities of the robots to perform introspection, i. e. , detect the malicious actions of compromised agents using other members of the group. In particular, the proposed solution finds multi-agent paths on discrete spaces combined with a set of mutual observations at specific locations to detect robots with significant deviations from the preordained routes. In this paper, we develop a planner that works on continuous configuration spaces while also taking into account similar spatio-temporal constraints. In addition, the planner allows for more general tasks that can be formulated as arbitrary smooth cost functions to be specified. The combination of constraints and objectives considered in this paper are not easily handled by popular path planning algorithms (e. g. , sampling-based methods), thus we propose a method based on the Alternating Direction Method of Multipliers (ADMM). ADMM is capable of finding locally optimal solutions to problems involving different kinds of objectives and non-convex temporal and spatial constraints, and allows for infeasible initialization. We benchmark our proposed method on multi-agent map exploration with minimum-uncertainty cost function, obstacles, and observation schedule constraints.

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AAMAS Conference 2019 Conference Paper

Masquerade Attack Detection Through Observation Planning for Multi-Robot Systems

  • Kacper Wardega
  • Roberto Tron
  • Wenchao Li

The increasing adoption of autonomous mobile robots comes with a rising concern over the security of these systems. In this work, we examine the dangers that an adversary could pose in a multi-agent robot system. We show that conventional multi-agent plans are vulnerable to strong attackers masquerading as a properly functioning agent. We propose a novel technique to incorporate attack detection into the multi-agent path-finding problem through the simultaneous synthesis of observation plans. We show that by specially crafting the multi-agent plan, the induced inter-agent observations can provide introspective monitoring guarantees; we achieve guarantees that any adversarial agent that plans to break the system-wide security specification must necessarily violate the induced observation plan.

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IROS Conference 2018 Conference Paper

Distributed Sensing Subject to Temporal Logic Constraints

  • Zachary T. Serlin
  • Kevin Leahy 0001
  • Roberto Tron
  • Calin Belta

This paper considers the combination of temporal logic (TL) specifications and local objective functions to create online, multiagent, motion plans. These plans are guaranteed to satisfy a persistent mission TL specification and locally optimize an objective function (e. g. in this paper, a cost based on information entropy). The presented approach decouples the two tasks by assigning sub-teams of agents to fulfill the TL specification, while unassigned agents optimize the objective function locally. This paper also presents a novel decoupling of the classic product automaton based approach while maintaining satisfaction guarantees. We also qualitatively show that optimality loss in the local greedy minimization due to the TL constraints can be approximated based on specification complexity. This approach is evaluated with a set of simulations and an experiment of 6 robots with real sensors.

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IROS Conference 2018 Conference Paper

Light-Weight Object Detection and Decision Making via Approximate Computing in Resource-Constrained Mobile Robots

  • Parul Pandey
  • Qifan He
  • Dario Pompili
  • Roberto Tron

Most of the current solutions for autonomous flights in indoor environments rely on purely geometric maps (e. g. , point clouds). There has been, however, a growing interest in supplementing such maps with semantic information (e. g. , object detections) using computer vision algorithms. Unfortunately, there is a disconnect between the relatively heavy computational requirements of these computer vision solutions, and the limited computation capacity available on mobile autonomous platforms. In this paper, we propose to bridge this gap with a novel Markov Decision Process framework that adapts the parameters of the vision algorithms to the incoming video data rather than fixing them a priori. As a concrete example, we test our framework on a object detection and tracking task, showing significant benefits in terms of energy consumption without considerable loss in accuracy, using a combination of publicly available and novel datasets.

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ICRA Conference 2018 Conference Paper

The Dynamic Bearing Observability Matrix Nonlinear Observability and Estimation for Multi-Agent Systems

  • Fabrizio Schiano
  • Roberto Tron

We consider the problem of localization in multiagent formations with bearing only measurements, and analyze the fundamental observability properties for dynamic agents. The current well-established approach is based on the socalled rigidity matrix, and its algebraic properties (e. g. , its rank and nullspace). This method is typically motivated using first-order derivatives, and shows, among other facts, that the global scale of the formation is not observable. This work shows that current results represent an incomplete view of the problem. In particular, we show that 1) current methods are a particular instantiation of nonlinear observability theory, 2) we can introduce the concept of the dynamic bearing observability matrix from higher order derivatives to study the observability of dynamic formations, and 3) the global scale is, in fact, generally observable when the agents move according to known inputs. We use tools from Riemannian geometry and Lie group theory to tackle, in a general and principled way, the general formulation of the localization problem with states that include both rotations and translations. Finally, we verify our theoretical results by deriving and applying, in both simulations and real experiments on UAVs, a centralized Extended Kalman Filter on Lie groups that is able to estimate the global scale of a moving formation.

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ICRA Conference 2015 Conference Paper

Initialization techniques for 3D SLAM: A survey on rotation estimation and its use in pose graph optimization

  • Luca Carlone
  • Roberto Tron
  • Kostas Daniilidis
  • Frank Dellaert

Pose graph optimization is the non-convex optimization problem underlying pose-based Simultaneous Localization and Mapping (SLAM). If robot orientations were known, pose graph optimization would be a linear least-squares problem, whose solution can be computed efficiently and reliably. Since rotations are the actual reason why SLAM is a difficult problem, in this work we survey techniques for 3D rotation estimation. Rotation estimation has a rich history in three scientific communities: robotics, computer vision, and control theory. We review relevant contributions across these communities, assess their practical use in the SLAM domain, and benchmark their performance on representative SLAM problems (Fig. 1). We show that the use of rotation estimation to bootstrap iterative pose graph solvers entails significant boost in convergence speed and robustness.

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ICRA Conference 2014 Conference Paper

An optimization approach to bearing-only visual homing with applications to a 2-D unicycle model

  • Roberto Tron
  • Kostas Daniilidis

We consider the problem of bearing-based visual homing: Given a mobile robot which can measure bearing directions corresponding to known landmarks, the goal is to guide the robot toward a desired “home” location. We propose a control law based on the gradient field of a Lyapunov function, and give sufficient conditions for global convergence. We show that the well-known Average Landmark Vector method (for which no convergence proof was known) can be obtained as a particular case of our framework. We then derive a sliding mode control law for a unicycle model which follows this gradient field. Both controllers do not depend on range information. Finally, we also show how our framework can be used to characterize the sensitivity of a home location with respect to noise in the specified bearings.

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