4 citations · 13 across the 39 of their papers we have counts for
44 papers
Co-designing a Layered Control Architecture
Chris Verhoek, Charis Stamouli, George J. Pappas +1
Layered control architectures (LCAs) are ubiquitous in complex engineering systems. However, in practice they are largely designed ad hoc and mostly dictated by the experience of t…
AnyRank: Reliable Anytime Policy Ranking via Sequential Testing
Apurva Badithela, David Snyder, Tenny Yin +6
Due to the high cost of hardware experiments, robot policies are predominantly evaluated by comparing their empirical performance on a limited number of hardware trials. However, f…
Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control
Antonio Pariente, Ignacio Boero, Nikolai Matni +1
World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we propose a JEPA-style world mode…
Policy Gradient over History-Dependent Policy Classes for LQR with Domain Randomization
Tesshu Fujinami, Bruce D. Lee, Anastasios Tsiamis +2
Domain Randomization (DR) has been widely used to overcome the sim-to-real gap by training a controller on a distribution of simulated environments via reinforcement learning. Whil…
Optimal input design via Frank-Wolfe
Fethi Bencherki, Bruce Lee, Nikolai Matni +1
We study optimal input design over a finite horizon for linear dynamical systems. The goal is to minimize a weighted inverse-covariance (information) criterion subject to an energy…
Robust Conformal CBF and CLF Controllers via Iterative Policy Updates
Omid Mirzaeedodangeh, Eliot Shekhtman, Nikolai Matni +1
Conformal prediction (CP) has been used to obtain probabilistic bounds on the error between a learned dynamics model and the true but unknown system. Such CP bounds can then be emb…
Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss
Thomas T. Zhang, Alok Shah, Yifei Zhang +3
Many modern applications of deep learning involve training a neural network via a one-step prediction loss (e.g., regression, cross-entropy), but deploy the network by rollin…
The Fragility of Learning LQG Controllers
Bruce D. Lee, Anastasios Tsiamis, Nikolai Matni +2
Learning methods are increasingly used to synthesize controllers from data, yet existing sample-complexity characterizations for continuous control are sharp only in the fully obse…
Near-optimal Online Traffic Engineering
Arvin Ghavidel, Pooria Namyar, Nikolai Matni +2
Most deployed WAN Traffic Engineering (TE) systems use a logically centralized controller that periodically gathers traffic demands, runs a TE optimization or heuristic, and then p…
A Quantitative Framework for Navigating Controller Design Tradeoffs under Computational Constraints
Chris Verhoek, Nikolai Matni
Computational constraints permeate the controller design process, and yet are rarely treated as explicit design constraints. Towards addressing this gap, we propose a quantitative…
Statistical Efficiency of Single- and Multi-step Models for Forecasting and Control
Anne Somalwar, Bruce D. Lee, George J. Pappas +1
Compounding error, where small prediction mistakes accumulate over time, presents a major challenge in learning-based control. A common remedy is to train multi-step predictors dir…
On Globally Optimal Stochastic Policy Gradient Methods for Domain Randomized LQR Synthesis
Alex Nguyen-Le, Nikolai Matni
Domain randomization is a simple, effective, and flexible scheme for obtaining robust feedback policies aimed at reducing the sim-to-real gap due to model mismatch. While domain ra…
Beyond Binary Success: Sample-Efficient and Statistically Rigorous Robot Policy Comparison
David Snyder, Apurva Badithela, Nikolai Matni +4
Generalist robot manipulation policies are becoming increasingly capable, but are limited in evaluation to a small number of hardware rollouts. This strong resource constraint in r…
Verification of Sequential Convex Programming for Parametric Non-convex Optimization
Rajiv Sambharya, Nikolai Matni, George Pappas
We introduce a verification framework to exactly verify the worst-case performance of sequential convex programming (SCP) algorithms for parametric non-convex optimization. The ver…
Safe Planning in Interactive Environments via Iterative Policy Updates and Adversarially Robust Conformal Prediction
Omid Mirzaeedodangeh, Eliot Shekhtman, Nikolai Matni +1
Safe planning of an autonomous agent in interactive environments -- such as the control of a self-driving vehicle among pedestrians -- poses a major challenge as the behavior of th…
Nearly Instance-Optimal Parameter Recovery from Many Trajectories via Hellinger Localization
Eliot Shekhtman, Yichen Zhou, Ingvar Ziemann +2
Learning from temporally-correlated data is a core facet of modern machine learning. Yet our understanding of sequential learning remains incomplete, particularly in the multi-traj…
Occupancy-aware Trajectory Planning for Autonomous Valet Parking in Uncertain Dynamic Environments
Farhad Nawaz, Faizan M. Tariq, Sangjae Bae +5
Autonomous Valet Parking (AVP) requires planning under partial observability, where parking spot availability evolves as dynamic agents enter and exit spots. Existing approaches ei…
Learning Acceleration Algorithms for Fast Parametric Convex Optimization with Certified Robustness
Rajiv Sambharya, Jinho Bok, Nikolai Matni +1
We develop a machine-learning framework to learn hyperparameter sequences for accelerated first-order methods (e.g., the step size and momentum sequences in accelerated gradient de…
Action Chunking and Exploratory Data Collection Yield Exponential Improvements in Behavior Cloning for Continuous Control
Thomas T. Zhang, Daniel Pfrommer, Chaoyi Pan +2
This paper presents a theoretical analysis of two of the most impactful interventions in modern learning from demonstration in robotics and continuous control: the practice of acti…
Quad-LCD: Layered Control Decomposition Enables Actuator-Feasible Quadrotor Trajectory Planning
Anusha Srikanthan, Hanli Zhang, Spencer Folk +2
In this work, we specialize contributions from prior work on data-driven trajectory generation for a quadrotor system with motor saturation constraints. When motors saturate in qua…
Graph-based Path Planning with Dynamic Obstacle Avoidance for Autonomous Parking
Farhad Nawaz, Minjun Sung, Darshan Gadginmath +6
Safe and efficient path planning in parking scenarios presents a significant challenge due to the presence of cluttered environments filled with static and dynamic obstacles. To ad…
Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error
Anne Somalwar, Bruce D. Lee, George J. Pappas +1
Compounding error, where small prediction mistakes accumulate over time, presents a major challenge in learning-based control. For example, this issue often limits the performance…
Policy Gradient for LQR with Domain Randomization
Tesshu Fujinami, Bruce D. Lee, Nikolai Matni +1
Domain randomization (DR) enables sim-to-real transfer by training controllers on a distribution of simulated environments, with the goal of achieving robust performance in the rea…
ADMM-MCBF-LCA: A Layered Control Architecture for Safe Real-Time Navigation
Anusha Srikanthan, Yifan Xue, Vijay Kumar +2
We consider the problem of safe real-time navigation of a robot in a dynamic environment with moving obstacles of arbitrary smooth geometries and input saturation constraints. We a…