no code implementations • 1 Dec 2023 • Yingzi Ma, Yulong Cao, Jiachen Sun, Marco Pavone, Chaowei Xiao
The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness.
no code implementations • 30 Nov 2023 • Yuxiao Chen, Sander Tonkens, Marco Pavone
Adept traffic models are critical to both planning and closed-loop simulation for autonomous vehicles (AV), and key design objectives include accuracy, diverse multimodal behaviors, interpretability, and downstream compatibility.
1 code implementation • 17 Nov 2023 • Jiageng Mao, Junjie Ye, Yuxi Qian, Marco Pavone, Yue Wang
Our approach, termed Agent-Driver, transforms the traditional autonomous driving pipeline by introducing a versatile tool library accessible via function calls, a cognitive memory of common sense and experiential knowledge for decision-making, and a reasoning engine capable of chain-of-thought reasoning, task planning, motion planning, and self-reflection.
1 code implementation • 9 Nov 2023 • Aaryan Singhal, Daniele Gammelli, Justin Luke, Karthik Gopalakrishnan, Dominik Helmreich, Marco Pavone
Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available cars to ride requests, rebalancing idle cars to areas of high demand, and charging vehicles to ensure sufficient range.
no code implementations • 7 Nov 2023 • Katie Z Luo, Xinshuo Weng, Yan Wang, Shuang Wu, Jie Li, Kilian Q Weinberger, Yue Wang, Marco Pavone
We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Encoder Representations from transFormers, to leverage priors in SD maps for the lane-topology prediction task.
no code implementations • 3 Nov 2023 • Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Seung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fidler, Marco Pavone, Yue Wang
We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes.
no code implementations • 27 Oct 2023 • Yuxiao Chen, Sushant Veer, Peter Karkus, Marco Pavone
In particular, IJP jointly optimizes over the behavior of the ego and the surrounding agents and leverages deep-learned prediction models as prediction priors that the join trajectory optimization tries to stay close to.
no code implementations • 15 Sep 2023 • Rohan Sinha, Edward Schmerling, Marco Pavone
When we rely on deep-learned models for robotic perception, we must recognize that these models may behave unreliably on inputs dissimilar from the training data, compromising the closed-loop system's safety.
no code implementations • 11 Sep 2023 • John Irvin Alora, Luis A. Pabon, Johannes Köhler, Mattia Cenedese, Ed Schmerling, Melanie N. Zeilinger, George Haller, Marco Pavone
Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time.
no code implementations • 1 Sep 2023 • Yulong Cao, Boris Ivanovic, Chaowei Xiao, Marco Pavone
This works aims to address this by developing a framework that employs reinforcement learning with human preference (RLHF) to enhance the realism of existing traffic models.
1 code implementation • 16 Jul 2023 • Shuhan Tan, Boris Ivanovic, Xinshuo Weng, Marco Pavone, Philipp Kraehenbuehl
In this work, we turn to language as a source of supervision for dynamic traffic scene generation.
no code implementations • 3 Jul 2023 • Sushant Veer, Apoorva Sharma, Marco Pavone
Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios.
no code implementations • 10 Jun 2023 • Ziyuan Zhong, Davis Rempe, Yuxiao Chen, Boris Ivanovic, Yulong Cao, Danfei Xu, Marco Pavone, Baishakhi Ray
Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development.
no code implementations • 18 May 2023 • Wenhao Ding, Tong Che, Ding Zhao, Marco Pavone
Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to its simplicity, flexibility, and off-policy nature.
1 code implementation • 16 May 2023 • Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems.
no code implementations • 3 Apr 2023 • Fan-Yun Sun, Jonathan Tremblay, Valts Blukis, Kevin Lin, Danfei Xu, Boris Ivanovic, Peter Karkus, Stan Birchfield, Dieter Fox, Ruohan Zhang, Yunzhu Li, Jiajun Wu, Marco Pavone, Nick Haber
At inference, given one or more views of a novel real-world object, FINV first finds a set of latent codes for the object by inverting the generative model from multiple initial seeds.
1 code implementation • 30 Mar 2023 • Thomas Lew, Riccardo Bonalli, Marco Pavone
We study the convex hulls of reachable sets of nonlinear systems with bounded disturbances.
1 code implementation • CVPR 2023 • Heng Yang, Marco Pavone
Geometric uncertainty propagation, on the other, propagates the geometric constraints on the keypoints to the 6D object pose, leading to a Pose UnceRtainty SEt (PURSE) that guarantees coverage of the groundtruth pose with the same probability.
2 code implementations • CVPR 2023 • Jiawei Yang, Marco Pavone, Yue Wang
One is to regularize the frequency range of NeRF's inputs, while the other is to penalize the near-camera density fields.
no code implementations • 28 Feb 2023 • Lucas Fuentes Valenzuela, Anthony Degleris, Abbas El Gamal, Marco Pavone, Ram Rajagopal
The method is model agnostic; it can compute LMEs for any convex optimization-based dispatch model, including some of the complex dispatch models employed by system operators in real electricity systems.
1 code implementation • 6 Feb 2023 • Spencer M. Richards, Jean-Jacques Slotine, Navid Azizan, Marco Pavone
Even for known nonlinear dynamical systems, feedback controller synthesis is a difficult problem that often requires leveraging the particular structure of the dynamics to induce a stable closed-loop system.
no code implementations • 28 Dec 2022 • Rohan Sinha, Apoorva Sharma, Somrita Banerjee, Thomas Lew, Rachel Luo, Spencer M. Richards, Yixiao Sun, Edward Schmerling, Marco Pavone
When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot autonomy stack.
1 code implementation • 14 Dec 2022 • Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer
We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system.
no code implementations • 13 Dec 2022 • Peter Karkus, Boris Ivanovic, Shie Mannor, Marco Pavone
To enable the joint optimization of AV stacks while retaining modularity, we present DiffStack, a differentiable and modular stack for prediction, planning, and control.
no code implementations • 2 Dec 2022 • 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 • 17 Nov 2022 • Rachel Luo, Rohan Sinha, Yixiao Sun, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone
When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical.
no code implementations • 9 Nov 2022 • Tarun Gupta, Peter Karkus, Tong Che, Danfei Xu, Marco Pavone
Effectively exploring the environment is a key challenge in reinforcement learning (RL).
no code implementations • 31 Oct 2022 • Ziyuan Zhong, Davis Rempe, Danfei Xu, Yuxiao Chen, Sushant Veer, Tong Che, Baishakhi Ray, Marco Pavone
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles.
no code implementations • 26 Oct 2022 • Matthew Tsao, Karthik Gopalakrishnan, Kaidi Yang, Marco Pavone
In this paper, we present a differentially private algorithm for network routing problems.
no code implementations • 26 Oct 2022 • Filippos Christianos, Peter Karkus, Boris Ivanovic, Stefano V. Albrecht, Marco Pavone
Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles.
no code implementations • 11 Oct 2022 • Ruixiang Zhang, Tong Che, Boris Ivanovic, Renhao Wang, Marco Pavone, Yoshua Bengio, Liam Paull
Humans are remarkably good at understanding and reasoning about complex visual scenes.
2 code implementations • 23 Sep 2022 • Boris Ivanovic, James Harrison, Marco Pavone
Learning-based behavior prediction methods are increasingly being deployed in real-world autonomous systems, e. g., in fleets of self-driving vehicles, which are beginning to commercially operate in major cities across the world.
no code implementations • 19 Sep 2022 • Yulong Cao, Chaowei Xiao, Anima Anandkumar, Danfei Xu, Marco Pavone
Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe driving behaviors.
1 code implementation • 14 Sep 2022 • Somrita Banerjee, Apoorva Sharma, Edward Schmerling, Max Spolaor, Michael Nemerouf, Marco Pavone
Algorithms within this framework are evaluated based on (1) model performance throughout mission lifetime and (2) cumulative costs associated with labeling and model retraining.
1 code implementation • 26 Aug 2022 • Danfei Xu, Yuxiao Chen, Boris Ivanovic, Marco Pavone
We empirically validate our method, named Bi-level Imitation for Traffic Simulation (BITS), with scenarios from two large-scale driving datasets and show that BITS achieves balanced traffic simulation performance in realism, diversity, and long-horizon stability.
no code implementations • 29 Jul 2022 • Yulong Cao, Danfei Xu, Xinshuo Weng, Zhuoqing Mao, Anima Anandkumar, Chaowei Xiao, Marco Pavone
We demonstrate that our method is able to improve the performance by 46% on adversarial data and at the cost of only 3% performance degradation on clean data, compared to the model trained with clean data.
1 code implementation • CVPR 2022 • Yuxiao Chen, Boris Ivanovic, Marco Pavone
In this work, we present ScePT, a policy planning-based trajectory prediction model that generates accurate, scene-consistent trajectory predictions suitable for autonomous system motion planning.
3 code implementations • 4 May 2022 • Robert Dyro, Edward Schmerling, Nikos Arechiga, Marco Pavone
Many existing approaches to bilevel optimization employ first-order sensitivity analysis, based on the implicit function theorem (IFT), for the lower problem to derive a gradient of the lower problem solution with respect to its parameters; this IFT gradient is then used in a first-order optimization method for the upper problem.
1 code implementation • 15 Apr 2022 • Mahmoud Selim, Amr Alanwar, Shreyas Kousik, Grace Gao, Marco Pavone, Karl H. Johansson
Reinforcement learning (RL) is capable of sophisticated motion planning and control for robots in uncertain environments.
1 code implementation • 14 Apr 2022 • 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 • 31 Mar 2022 • Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan, Ramesh Johari, Marco Pavone
at each period, we show that our approach obtains an expected regret and road capacity violation of $O(\sqrt{T})$, where $T$ is the number of periods over which tolls are updated.
2 code implementations • 15 Mar 2022 • Tim Salzmann, Elia Kaufmann, Jon Arrizabalaga, Marco Pavone, Davide Scaramuzza, Markus Ryll
Our experiments, performed in simulation and the real world onboard a highly agile quadrotor platform, demonstrate the capabilities of the described system to run learned models with, previously infeasible, large modeling capacity using gradient-based online optimization MPC.
1 code implementation • CVPR 2022 • Tim Salzmann, Marco Pavone, Markus Ryll
We present Motron, a multimodal, probabilistic, graph-structured model, that captures human's multimodality using probabilistic methods while being able to output deterministic maximum-likelihood motions and corresponding confidence values for each mode.
no code implementations • 6 Mar 2022 • Robin Brown, Edward Schmerling, Navid Azizan, Marco Pavone
Verifying that input-output relationships of a neural network conform to prescribed operational specifications is a key enabler towards deploying these networks in safety-critical applications.
1 code implementation • 15 Feb 2022 • Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs.
no code implementations • 10 Feb 2022 • Devansh Jalota, Michael Ostrovsky, Marco Pavone
To this end, we first consider the setting when the number of institutions (e. g., firms in a labor market) is one and show that equilibrium arrangements exist irrespective of the nature of the constraint structure or the agents' preferences.
no code implementations • 8 Feb 2022 • Adam J. Thorpe, Thomas Lew, Meeko M. K. Oishi, Marco Pavone
We present a data-driven algorithm for efficiently computing stochastic control policies for general joint chance constrained optimal control problems.
no code implementations • CVPR 2022 • Xinshuo Weng, Boris Ivanovic, Kris Kitani, Marco Pavone
This is typically caused by the propagation of errors from tracking to prediction, such as noisy tracks, fragments, and identity switches.
2 code implementations • 10 Dec 2021 • Thomas Lew, Lucas Janson, Riccardo Bonalli, Marco Pavone
In this work, we analyze an efficient sampling-based algorithm for general-purpose reachability analysis, which remains a notoriously challenging problem with applications ranging from neural network verification to safety analysis of dynamical systems.
no code implementations • 2 Dec 2021 • Manabu Nakanoya, Junha Im, Hang Qiu, Sachin Katti, Marco Pavone, Sandeep Chinchali
Autonomous vehicles (AVs) must interact with a diverse set of human drivers in heterogeneous geographic areas.
no code implementations • 11 Nov 2021 • Thomas Lew, Apoorva Sharma, James Harrison, Edward Schmerling, Marco Pavone
We identify an issue in recent approaches to learning-based control that reformulate systems with uncertain dynamics using a stochastic differential equation.
1 code implementation • 18 Oct 2021 • Xinshuo Weng, Boris Ivanovic, Marco Pavone
Recently, there has been tremendous progress in developing each individual module of the standard perception-planning robot autonomy pipeline, including detection, tracking, prediction of other agents' trajectories, and ego-agent trajectory planning.
1 code implementation • 17 Oct 2021 • Shushman Choudhury, Kiril Solovey, Mykel Kochenderfer, Marco Pavone
The second stage solves only for drones, by routing them over a composite of the road network and the transit network defined by truck paths from the first stage.
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.
1 code implementation • 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 • NeurIPS 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 towards guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction metrics (CCM) that aims to minimize the $\mathcal L_\infty$ gain from the disturbances to nominal-actual trajectory deviations.
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 • Karen Leung, Andrea Bajcsy, 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 recently been 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.
1 code implementation • 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.
2 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.
no code implementations • 22 Feb 2021 • Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone
In this work, we propose the local calibration error (LCE) to span the gap between average and individual reliability.
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 • 6 Dec 2020 • Joseph Lorenzetti, Andrew McClellan, Charbel Farhat, Marco Pavone
Model predictive controllers use dynamics models to solve constrained optimal control problems.
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.
no code implementations • NeurIPS Workshop LMCA 2020 • Abhishek Cauligi, Preston Culbertson, Mac Schwager, Bartolomeo Stellato, Marco Pavone
Mixed-integer convex programming (MICP) is a popular modeling framework for solving discrete and combinatorial optimization problems arising in various settings.
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 • 19 Aug 2020 • Gioele Zardini, Nicolas Lanzetti, Andrea Censi, Emilio Frazzoli, Marco Pavone
This requires tools to study such a coupling and co-design mobility systems in terms of different objectives.
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.
4 code implementations • 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 #2 on
Trajectory Prediction
on ETH
2 code implementations • 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