61 citations · 243 across the 64 of their papers we have counts for
34 papers · 1 filter
Inertia-Corrected Newton Method For Generalized Nash Equilibria in Dynamic Games with Optimality Verification
Zhiyuan Zhang, Panagiotis Tsiotras
Newton methods efficiently find Generalized Nash Equilibria (GNE) in dynamic games by solving for the KKT necessary conditions. These methods are fast and can support multi-agent M…
Game-Theoretic Control with Constrained Potential Surgery
Zhiyuan Zhang, Panagiotis Tsiotras
Constrained general-sum dynamic games are a popular formulation for highly interactive multi-agent planning problems. In recent years, Generalized Nash Equilibrium (GNE) solvers ha…
Online, Reachability-Aware, Sampling-Based Motion Planning
Brendan Gould, Zhiyuan Zhang, Panagiotis Tsiotras +1
Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. Historically, such approaches h…
Delayed Expansion AGT: Kinodynamic Planning with Application to Tractor-Trailer Parking
Dongliang Zheng, Yebin Wang, Stefano Di Cairano +1
Kinodynamic planning of articulated vehicles in cluttered environments faces additional challenges arising from high-dimensional state space and complex system dynamics. Built upon…
Chance-Constrained Information-Theoretic Stochastic Model Predictive Control with Safety Shielding
Ji Yin, Panagiotis Tsiotras, Karl Berntorp
This paper introduces a novel nonlinear stochastic model predictive control path integral (MPPI) method, which considers chance constraints on system states. The proposed belief-sp…
Multi-agent Task-Driven Exploration via Intelligent Map Compression and Sharing
Evangelos Psomiadis, Dipankar Maity, Panagiotis Tsiotras
This paper investigates the task-driven exploration of unknown environments with mobile sensors communicating compressed measurements. The sensors explore the area and transmit the…
Active Learning with Dual Model Predictive Path-Integral Control for Interaction-Aware Autonomous Highway On-ramp Merging
Jacob Knaup, Jovin D'sa, Behdad Chalaki +4
Merging into dense highway traffic for an autonomous vehicle is a complex decision-making task, wherein the vehicle must identify a potential gap and coordinate with surrounding hu…
Kinodynamic FMT* with Dimensionality Reduction Heuristics and Neural Network Controllers
Dongliang Zheng, Panagiotis Tsiotras
This paper proposes a new sampling-based kinodynamic motion planning algorithm, called FMT*PFF, for nonlinear systems. It exploits the novel idea of dimensionality reduction using…
IBBT: Informed Batch Belief Trees for Motion Planning Under Uncertainty
Dongliang Zheng, Panagiotis Tsiotras
In this work, we propose the Informed Batch Belief Trees (IBBT) algorithm for motion planning under motion and sensing uncertainties. The original stochastic motion planning proble…
Non-Trivial Query Sampling For Efficient Learning To Plan
Sagar Suhas Joshi, Panagiotis Tsiotras
In recent years, learning-based approaches have revolutionized motion planning. The data generation process for these methods involves caching a large number of high quality paths…
Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Approach Using Control Barrier Functions
Ji Yin, Charles Dawson, Chuchu Fan +1
Model Predictive Path Integral (MPPI) control is a type of sampling-based model predictive control that simulates thousands of trajectories and uses these trajectories to synthesiz…
AstroSLAM: Autonomous Monocular Navigation in the Vicinity of a Celestial Small Body -- Theory and Experiments
Mehregan Dor, Travis Driver, Kenneth Getzandanner +1
We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown target small celestial body. AstroSLAM is predicated on the formulation…
Lazy Incremental Search for Efficient Replanning with Bounded Suboptimality Guarantees
Jaein Lim, Mahdi Ghanei, R. Connor Lawson +2
We present a lazy incremental search algorithm, Lifelong-GLS (L-GLS), along with its bounded suboptimal version, Bounded L-GLS (B-LGLS) that combine the search efficiency of increm…
Risk-Aware Model Predictive Path Integral Control Using Conditional Value-at-Risk
Ji Yin, Zhiyuan Zhang, Panagiotis Tsiotras
In this paper, we present a novel Model Predictive Control method for autonomous robots subject to arbitrary forms of uncertainty. The proposed Risk-Aware Model Predictive Path Int…
Information-theoretic Abstraction of Semantic Octree Models for Integrated Perception and Planning
Daniel T. Larsson, Arash Asgharivaskasi, Jaein Lim +2
In this paper, we develop an approach that enables autonomous robots to build and compress semantic environment representations from point-cloud data. Our approach builds a three-d…
Simultaneous Control and Trajectory Estimation for Collision Avoidance of Autonomous Robotic Spacecraft Systems
Matthew King-Smith, Panagiotis Tsiotras, Frank Dellaert
We propose factor graph optimization for simultaneous planning, control, and trajectory estimation for collision-free navigation of autonomous systems in environments with moving o…
Batch Belief Trees for Motion Planning Under Uncertainty
Dongliang Zheng, Panagiotis Tsiotras
In this work, we develop the Batch Belief Trees (BBT) algorithm for motion planning under motion and sensing uncertainties. The algorithm interleaves between batch sampling, buildi…
Trajectory Distribution Control for Model Predictive Path Integral Control using Covariance Steering
Ji Yin, Zhiyuan Zhang, Evangelos Theodorou +1
This paper presents a novel control approach for autonomous systems operating under uncertainty. We combine Model Predictive Path Integral (MPPI) control with Covariance Steering (…
Lazy Lifelong Planning for Efficient Replanning in Graphs with Expensive Edge Evaluation
Jaein Lim, Siddhartha Srinivasa, Panagiotis Tsiotras
We present an incremental search algorithm, called Lifelong-GLS, which combines the vertex efficiency of Lifelong Planning A* (LPA*) and the edge efficiency of Generalized Lazy Sea…
Belief Space Planning: A Covariance Steering Approach
Dongliang Zheng, Jack Ridderhof, Panagiotis Tsiotras +1
A new belief space planning algorithm, called covariance steering Belief RoadMap (CS-BRM), is introduced, which is a multi-query algorithm for motion planning of dynamical systems…
LES: Locally Exploitative Sampling for Robot Path Planning
Sagar Suhas Joshi, Seth Hutchinson, Panagiotis Tsiotras
Sampling-based algorithms solve the path planning problem by generating random samples in the search-space and incrementally growing a connectivity graph or a tree. Conventionally,…
Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming
Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
In this paper, a mixed-integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, graph abstractions for resource-constrained agents is pr…
A Generalized A* Algorithm for Finding Globally Optimal Paths in Weighted Colored Graphs
Jaein Lim, Panagiotis Tsiotras
Both geometric and semantic information of the search space is imperative for a good plan. We encode those properties in a weighted colored graph (geometric information in terms of…
Information-Theoretic Abstractions for Planning in Agents with Computational Constraints
Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
In this paper, we develop a framework for path-planning on abstractions that are not provided to the agent a priori but instead emerge as a function of the available computational…