42 citations · 88 across the 19 of their papers we have counts for
20 papers
Distributionally Robust Adaptive Iterative Covariance Steering for Small-Body Proximity Operations
Vivek Khatana, Aditya Gahlawat, Naira Hovakimyan +1
Autonomous small-body proximity operations require continuous-thrust guidance under uncertain gravity, solar-radiation pressure (SRP), and stochastic disturbances. We combine itera…
Distributionally Robust Planning with Adaptive Control
Astghik Hakobyan, Amaras Nazarians, Aditya Gahlawat +2
Safe operation of autonomous systems requires robustness to both model uncertainty and uncertainty in the environment. We propose DRP-AC, a hierarchical framework fo…
Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
Aditya Gahlawat, Ahmed Aboudonia, Sandeep Banik +5
Imitation learning (IL) enables autonomous behavior by learning from expert demonstrations. While more sample-efficient than comparative alternatives like reinforcement learning, I…
-DRAC: Distributionally Robust Adaptive Control
Aditya Gahlawat, Sambhu H. Karumanchi, Naira Hovakimyan
Data-driven machine learning methodologies have attracted considerable attention for the control and estimation of dynamical systems. However, such implementations suffer from a la…
Wasserstein Distributionally Robust Adaptive Covariance Steering
Aditya Gahlawat, Vivek Khatana, Duo Wang +3
We present a methodology for predictable and safe covariance steering control of uncertain nonlinear stochastic processes. The systems under consideration are subject to general un…
Robust Model Based Reinforcement Learning Using Adaptive Control
Minjun Sung, Sambhu H. Karumanchi, Aditya Gahlawat +1
We introduce -MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms l…
Quad: Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees
Zhuohuan Wu, Sheng Cheng, Pan Zhao +6
Quadrotors that can operate predictably in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications. We present L1Quad, a con…
Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation
Pan Zhao, Ziyao Guo, Yikun Cheng +3
This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation for nonlinear systems subject to matched uncertaintie…
Adaptive Augmentation for Geometric Tracking Control of Quadrotors
Zhuohuan Wu, Sheng Cheng, Kasey A. Ackerman +4
This paper introduces an adaptive control augmentation for geometric tracking control of quadrotors. In the proposed design, the augmentation handle…
Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics
Pan Zhao, Arun Lakshmanan, Kasey Ackerman +3
This paper presents an approach towards guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction met…
Safe Sampling-Based Air-Ground Rendezvous Algorithm for Complex Urban Environments
Gabriel Barsi Haberfeld, Aditya Gahlawat, Naira Hovakimyan
Demand for fast and economical parcel deliveries in urban environments has risen considerably in recent years. A framework envisions efficient last-mile delivery in urban environme…
Distributed Algorithms for Linearly-Solvable Optimal Control in Networked Multi-Agent Systems
Neng Wan, Aditya Gahlawat, Naira Hovakimyan +2
Distributed algorithms for both discrete-time and continuous-time linearly solvable optimal control (LSOC) problems of networked multi-agent systems (MASs) are investigated in this…
Cooperative Path Integral Control for Stochastic Multi-Agent Systems
Neng Wan, Aditya Gahlawat, Naira Hovakimyan +2
A distributed stochastic optimal control solution is presented for cooperative multi-agent systems. The network of agents is partitioned into multiple factorial subsystems, each of…
Compositionality of Linearly Solvable Optimal Control in Networked Multi-Agent Systems
Lin Song, Neng Wan, Aditya Gahlawat +2
In this paper, we discuss the methodology of generalizing the optimal control law from learned component tasks to unlearned composite tasks on Multi-Agent Systems (MASs), by using…
Contraction -Adaptive Control using Gaussian Processes
Aditya Gahlawat, Arun Lakshmanan, Lin Song +4
We present -, a control framework that enables safe simultaneous learning and control for systems subject to uncertainties. The two main constituents…
Safe Feedback Motion Planning: A Contraction Theory and -Adaptive Control Based Approach
Arun Lakshmanan, Aditya Gahlawat, Naira Hovakimyan
Autonomous robots that are capable of operating safely in the presence of imperfect model knowledge or external disturbances are vital in safety-critical applications. In this pape…
-: Adaptive Control with Bayesian Learning
Aditya Gahlawat, Pan Zhao, Andrew Patterson +2
We present -, an architecture based on adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learnin…
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction
Andrew Patterson, Aditya Gahlawat, Naira Hovakimyan
Autonomous agents must be able to safely interact with other vehicles to integrate into urban environments. The safety of these agents is dependent on their ability to predict coll…
Risk Sensitive Rendezvous Algorithm for Heterogeneous Agents in Urban Environments
Gabriel Barsi Haberfeld, Aditya Gahlawat, Naira Hovakimyan
Demand for fast and inexpensive parcel deliveries in urban environments has risen considerably in recent years. A framework is envisioned to enforce efficient last mile delivery in…
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
Hyung-Jin Yoon, Huaiyu Chen, Kehan Long +4
We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observat…