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1 code implementation • 7 Oct 2021 • Boris Ivanovic, Yifeng Lin, Shubham Shrivastava, Punarjay Chakravarty, Marco Pavone

As a result, perceptual uncertainties are not propagated through forecasting and predictions are frequently overconfident.

no code implementations • 7 Oct 2021 • Boris Ivanovic, Marco Pavone

Detecting other agents and forecasting their behavior is an integral part of the modern robotic autonomy stack, especially in safety-critical scenarios entailing human-robot interaction such as autonomous driving.

1 code implementation • 29 Sep 2021 • Jiangnan Cheng, Marco Pavone, Sachin Katti, Sandeep Chinchali, Ao Tang

Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation.

no code implementations • 28 Sep 2021 • Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone

When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial.

1 code implementation • 9 Sep 2021 • Pan Zhao, Arun Lakshmanan, Kasey Ackerman, Aditya Gahlawat, Marco Pavone, Naira Hovakimyan

This paper presents an approach to guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction metrics (CCM) that aim to minimize the $\mathcal{L}_\infty$ gain from the disturbances to the deviation of actual variables of interests from their nominal counterparts.

no code implementations • 3 Sep 2021 • Nicolas Lanzetti, Maximilian Schiffer, Michael Ostrovsky, Marco Pavone

Cities worldwide struggle with overloaded transportation systems and their externalities, such as traffic congestion and emissions.

no code implementations • 30 Jul 2021 • Andrea Bajcsy, Karen Leung, Edward Schmerling, Marco Pavone

As safety-critical autonomous vehicles (AVs) will soon become pervasive in our society, a number of safety concepts for trusted AV deployment have been recently proposed throughout industry and academia.

no code implementations • 29 Jul 2021 • John Willes, James Harrison, Ali Harakeh, Chelsea Finn, Marco Pavone, Steven Waslander

As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information.

no code implementations • 21 Jul 2021 • Boris Ivanovic, Marco Pavone

Forecasting the behavior of other agents is an integral part of the modern robotic autonomy stack, especially in safety-critical scenarios with human-robot interaction, such as autonomous driving.

no code implementations • 1 Jul 2021 • Justin Luke, Mauro Salazar, Ram Rajagopal, Marco Pavone

Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems.

no code implementations • 28 Jun 2021 • Gioele Zardini, Nicolas Lanzetti, Marco Pavone, Emilio Frazzoli

We provide a comprehensive review of methods and tools to model and solve problems related to Autonomous Mobility-on-Demand systems.

no code implementations • 26 Apr 2021 • Boris Ivanovic, Kuan-Hui Lee, Pavel Tokmakov, Blake Wulfe, Rowan Mcallister, Adrien Gaidon, Marco Pavone

Reasoning about the future behavior of other agents is critical to safe robot navigation.

1 code implementation • 23 Apr 2021 • Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone

Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles.

no code implementations • 16 Apr 2021 • Rohan Sinha, James Harrison, Spencer M. Richards, Marco Pavone

We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component.

no code implementations • 6 Apr 2021 • Robert Dyro, James Harrison, Apoorva Sharma, Marco Pavone

As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essential for robust performance.

no code implementations • 31 Mar 2021 • Devansh Jalota, Kiril Solovey, Matthew Tsao, Stephen Zoepf, Marco Pavone

To address the inherent unfairness of SO routing, we study the ${\beta}$-fair SO problem whose goal is to minimize the total travel time while guaranteeing a ${\beta\geq 1}$ level of unfairness, which specifies the maximum possible ratio between the travel times of different users with shared origins and destinations.

1 code implementation • 7 Mar 2021 • Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine, Marco Pavone

Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments.

1 code implementation • 24 Feb 2021 • Apoorva Sharma, Navid Azizan, Marco Pavone

In order to safely deploy Deep Neural Networks (DNNs) within the perception pipelines of real-time decision making systems, there is a need for safeguards that can detect out-of-training-distribution (OoD) inputs both efficiently and accurately.

1 code implementation • 28 Jan 2021 • Margaret P. Chapman, Riccardo Bonalli, Kevin M. Smith, Insoon Yang, Marco Pavone, Claire J. Tomlin

In addition, we propose a second definition for risk-sensitive safe sets and provide a tractable method for their estimation without using a parameter-dependent upper bound.

no code implementations • 12 Dec 2020 • Sandeep Chinchali, Evgenya Pergament, Manabu Nakanoya, Eyal Cidon, Edward Zhang, Dinesh Bharadia, Marco Pavone, Sachin Katti

Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road construction sites, to steadily improve robotic perception models.

1 code implementation • 2 Dec 2020 • Simon Schaefer, Karen Leung, Boris Ivanovic, Marco Pavone

To achieve seamless human-robot interactions, robots need to intimately reason about complex interaction dynamics and future human behaviors within their motion planning process.

no code implementations • 6 Nov 2020 • Manabu Nakanoya, Sandeep Chinchali, Alexandros Anemogiannis, Akul Datta, Sachin Katti, Marco Pavone

However, today's representations for sensory data are mostly designed for human, not robotic, perception and thus often waste precious compute or wireless network resources to transmit unimportant parts of a scene that are unnecessary for a high-level robotic task.

1 code implementation • NeurIPS 2020 • Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, Marco Pavone

Discrete latent spaces in variational autoencoders have been shown to effectively capture the data distribution for many real-world problems such as natural language understanding, human intent prediction, and visual scene representation.

1 code implementation • 16 Sep 2020 • Boris Ivanovic, Amine Elhafsi, Guy Rosman, Adrien Gaidon, Marco Pavone

Reasoning about human motion is a core component of modern human-robot interactive systems.

no code implementations • 12 Sep 2020 • Haruki Nishimura, Boris Ivanovic, Adrien Gaidon, Marco Pavone, Mac Schwager

This paper presents a novel online framework for safe crowd-robot interaction based on risk-sensitive stochastic optimal control, wherein the risk is modeled by the entropic risk measure.

no code implementations • 26 Aug 2020 • Thomas Lew, Apoorva Sharma, James Harrison, Andrew Bylard, Marco Pavone

In this work, we propose a practical and theoretically-justified approach to maintaining safety in the presence of dynamics uncertainty.

no code implementations • 10 Aug 2020 • Boris Ivanovic, Karen Leung, Edward Schmerling, Marco Pavone

Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms.

1 code implementation • 31 Jul 2020 • Karen Leung, Nikos Aréchiga, Marco Pavone

This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs.

1 code implementation • ECCV 2020 • Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, Marco Pavone

Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation.

Ranked #10 on Trajectory Prediction on nuScenes

1 code implementation • NeurIPS 2020 • James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone

In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.

2 code implementations • 26 Sep 2019 • Shushman Choudhury, Kiril Solovey, Mykel J. Kochenderfer, Marco Pavone

Our results show that the framework computes solutions typically within a few seconds on commodity hardware, and that drones travel up to $360 \%$ of their flight range with public transit.

1 code implementation • 29 Jul 2019 • Sumeet Singh, Spencer M. Richards, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone

We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.

no code implementations • NeurIPS 2019 • Jonathan Lacotte, Mert Pilanci, Marco Pavone

We propose a new randomized optimization method for high-dimensional problems which can be seen as a generalization of coordinate descent to random subspaces.

1 code implementation • 1 May 2019 • Alvaro Estandia, Maximilian Schiffer, Federico Rossi, Justin Luke, Emre Can Kara, Ram Rajagopal, Marco Pavone

Specifically, we extend previous results on an optimization-based modeling approach for electric AMoD systems to jointly control an electric AMoD fleet and a series of PDNs, and analyze the benefit of coordination under load balancing constraints.

no code implementations • 15 Feb 2019 • Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone

In this paper, we formulate a novel Robot Offloading Problem --- how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication?

no code implementations • 9 Jan 2019 • Apoorva Sharma, James Harrison, Matthew Tsao, Marco Pavone

The first, RAMCP-F, converges to an optimal risk-sensitive policy without having to rebuild the search tree as the underlying belief over models is perturbed.

1 code implementation • ICCV 2019 • Boris Ivanovic, Marco Pavone

Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society.

no code implementations • 13 Aug 2018 • Jonathan Lacotte, Mohammad Ghavamzadeh, Yin-Lam Chow, Marco Pavone

We then derive two different versions of our RS-GAIL optimization problem that aim at matching the risk profiles of the agent and the expert w. r. t.

no code implementations • 31 Jul 2018 • Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone

We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.

3 code implementations • 24 Jul 2018 • James Harrison, Apoorva Sharma, Marco Pavone

However, this approach suffers from two main drawbacks: (1) it is computationally inefficient, as computation scales poorly with the number of samples; and (2) it can be data inefficient, as encoding prior knowledge that can aid the model through the choice of kernel and associated hyperparameters is often challenging and unintuitive.

1 code implementation • 16 Jun 2018 • Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, Marco Pavone

Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance.

no code implementations • 16 Apr 2018 • Lucas Janson, Tommy Hu, Marco Pavone

This paper addresses the problem of planning a safe (i. e., collision-free) trajectory from an initial state to a goal region when the obstacle space is a-priori unknown and is incrementally revealed online, e. g., through line-of-sight perception.

1 code implementation • 6 Mar 2018 • Boris Ivanovic, Edward Schmerling, Karen Leung, Marco Pavone

This work presents a methodology for modeling and predicting human behavior in settings with N humans interacting in highly multimodal scenarios (i. e. where there are many possible highly-distinct futures).

Robotics Human-Computer Interaction

1 code implementation • 28 Nov 2017 • Sumeet Singh, Jonathan Lacotte, Anirudha Majumdar, Marco Pavone

The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i. e., that humans are risk neutral.

no code implementations • 30 Oct 2017 • Anirudha Majumdar, Marco Pavone

We discuss general representation theorems that precisely characterize the class of metrics that satisfy these axioms (referred to as distortion risk metrics), and provide instantiations that can be used in applications.

1 code implementation • 25 Oct 2017 • Edward Schmerling, Karen Leung, Wolf Vollprecht, Marco Pavone

This paper presents a method for constructing human-robot interaction policies in settings where multimodality, i. e., the possibility of multiple highly distinct futures, plays a critical role in decision making.

no code implementations • 20 Sep 2017 • Ramon Iglesias, Federico Rossi, Kevin Wang, David Hallac, Jure Leskovec, Marco Pavone

The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i. e. fleets of self-driving vehicles).

Robotics Multiagent Systems Systems and Control Applications

2 code implementations • 16 Sep 2017 • Brian Ichter, James Harrison, Marco Pavone

This paper proposes a methodology for non-uniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling.

1 code implementation • 14 Sep 2017 • Federico Rossi, Ramon Iglesias, Mahnoosh Alizadeh, Marco Pavone

We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network.

Systems and Control Multiagent Systems Robotics

no code implementations • 6 Jul 2016 • Masahiro Ono, Mahmoud El Chamie, Marco Pavone, Behcet Acikmese

We found that the same result holds for stochastic optimal control problems with continuous state and action spaces. Furthermore, we show the randomization of control input can result in reduced cost when the optimization problem is nonconvex, and the cost reduction is equal to the duality gap.

no code implementations • 5 Dec 2015 • Yin-Lam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone

In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account \emph{risk}, i. e., increased awareness of events of small probability and high consequences.

no code implementations • 29 Sep 2015 • Yin-Lam Chow, Jia Yuan Yu, Marco Pavone

We consider one-way vehicle sharing systems where customers can rent a car at one station and drop it off at another.

no code implementations • NeurIPS 2015 • Yin-Lam Chow, Aviv Tamar, Shie Mannor, Marco Pavone

Our first contribution is to show that a CVaR objective, besides capturing risk sensitivity, has an alternative interpretation as expected cost under worst-case modeling errors, for a given error budget.

1 code implementation • 30 Apr 2015 • Lucas Janson, Edward Schmerling, Marco Pavone

MCMP applies this CP estimation procedure to motion planning by iteratively (i) computing an (approximately) optimal path for the deterministic version of the problem (here, using the FMT* algorithm), (ii) computing the CP of this path, and (iii) inflating or deflating the obstacles by a common factor depending on whether the CP is higher or lower than a target value.

Robotics

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