2 citations · 2 across the 19 of their papers we have counts for
25 papers
Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning
Prabin Kumar Rath, Omkar Patil, Nakul Gopalan
Behavior cloning (BC) in non-Markovian environments is a challenging problem because policies have to reason over contextual information over long horizons. Existing policy archite…
Adaptive Control as an Information Gradient Flow: Excitation, Constraints, Energy, and Stochastic Learning
Omkar Sudhir Patil
Adaptive control is usually developed through Lyapunov stability, excitation, and parameter convergence, whereas neighboring fields describe learning using convexity, information g…
Decentralized Scalar Field Mapping using Gaussian Process
Hossein Papi, Muzaffar Qureshi, Kyle Volle +2
Decentralized Gaussian process (GP) methods provide a scalable framework for multi-agent scalar-field estimation by allowing each agent to maintain a local GP over its assigned sub…
Nesterov-Accelerated Concurrent Learning for Lyapunov-Based Deep Neural Networks
Hossein Papi, Omkar Sudhir Patil
Concurrent learning (CL) has recently been extended to Lyapunov-based deep neural networks (DNNs) to obtain parameter convergence under finite rather than persistent excitation. Ho…
Neural ODEs Meet Concurrent Learning: Stable Online Learning with Lyapunov Guarantees
Omkar Sudhir Patil
Neural ODEs learn dynamics from trajectory losses, but their adjoint gradients lack the regressor-times-parameter-error structure on which Lyapunov analyses of online adaptation re…
Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation
Swagat Padhan, Lakshya Jain, Bhavya Minesh Shah +3
Robots collaborating with humans must convert natural language goals into actionable, physically grounded decisions. For example, executing a command such as "go two meters to the…
StageCraft: Execution Aware Mitigation of Distractor and Obstruction Failures in VLA Models
Kartikay Milind Pangaonkar, Prabin Rath, Omkar Patil +1
Large scale pre-training on text and image data along with diverse robot demonstrations has helped Vision Language Action models (VLAs) to generalize to novel tasks, objects and sc…
You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector
Omkar Patil, Ondrej Biza, Thomas Weng +9
Generative robot policies trained on demonstrations using behavior cloning often learn actions that are sub-optimal or misaligned with respect to the downstream task. Policy improv…
PokeNet: Learning Kinematic Models of Articulated Objects from Human Observations
Anmol Gupta, Weiwei Gu, Omkar Patil +2
Articulation modeling enables robots to learn joint parameters of articulated objects for effective manipulation which can then be used downstream for skill learning or planning. E…
Riemannian Lyapunov Optimizer: A Unified Framework for Optimization
Yixuan Wang, Omkar Sudhir Patil, Warren E. Dixon
We introduce Riemannian Lyapunov Optimizers (RLOs), a family of optimization algorithms that unifies classic optimizers within one geometric framework. Unlike heuristic improvement…
Factorizing Diffusion Policies for Observation Modality Prioritization
Omkar Patil, Prabin Rath, Kartikay Pangaonkar +2
Diffusion models have been extensively leveraged for learning robot skills from demonstrations. These policies are conditioned on several observational modalities such as proprioce…
LyLA-Therm: Lyapunov-based Langevin Adaptive Thermodynamic Neural Network Controller
Saiedeh Akbari, Omkar Sudhir Patil, Warren E. Dixon
Thermodynamic principles can be employed to design parameter update laws that address challenges such as the exploration vs. exploitation dilemma. In this paper, inspired by the La…
System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach
Rebecca G. Hart, Omkar Sudhir Patil, Zachary I. Bell +1
Deep Neural Networks (DNNs) are increasingly used in control applications due to their powerful function approximation capabilities. However, many existing formulations focus prima…
Learning Sequential Kinematic Models from Demonstrations for Multi-Jointed Articulated Objects
Anmol Gupta, Weiwei Gu, Omkar Patil +2
As robots become more generalized and deployed in diverse environments, they must interact with complex objects, many with multiple independent joints or degrees of freedom (DoF) r…
Lyapunov-Based Graph Neural Networks for Adaptive Control of Multi-Agent Systems
Brandon C. Fallin, Cristian F. Nino, Omkar Sudhir Patil +2
Graph neural networks (GNNs) have a message-passing framework in which vector messages are exchanged between graph nodes and updated using feedforward layers. The inclusion of dist…
Bounds on Deep Neural Network Partial Derivatives with Respect to Parameters
Omkar Sudhir Patil, Brandon C. Fallin, Cristian F. Nino +2
Deep neural networks (DNNs) have emerged as a powerful tool with a growing body of literature exploring Lyapunov-based approaches for real-time system identification and control. T…
Distributed RISE-based Control for Exponential Heterogeneous Multi-Agent Target Tracking of Second-Order Nonlinear Systems
Cristian F. Nino, Omkar Sudhir Patil, Sage C. Edwards +1
A distributed implementation of a Robust Integral of the Sign of the Error (RISE) controller is developed for multi-agent target tracking problems with exponential convergence guar…
Collaborative Spacecraft Servicing under Partial Feedback using Lyapunov-based Deep Neural Networks
Cristian F. Nino, Omkar Sudhir Patil, Christopher D. Petersen +2
Multi-agent systems are increasingly applied in space missions, including distributed space systems, resilient constellations, and autonomous rendezvous and docking operations. A c…
Lyapunov-Based Deep Neural Networks for Adaptive Control of Stochastic Nonlinear Systems
Saiedeh Akbari, Cristian F. Nino, Omkar Sudhir Patil +1
Controlling nonlinear stochastic dynamical systems involves substantial challenges when the dynamics contain unknown and unstructured nonlinear state-dependent terms. For such comp…
Composing Diffusion Policies for Few-shot Learning of Movement Trajectories
Omkar Patil, Anant Sah, Nakul Gopalan
Humans can perform various combinations of physical skills without having to relearn skills from scratch every single time. For example, we can swing a bat when walking without hav…
Adaptive Deep Neural Network-Based Control Barrier Functions
Hannah M. Sweatland, Omkar Sudhir Patil, Warren E. Dixon
Safety constraints of nonlinear control systems are commonly enforced through the use of control barrier functions (CBFs). Uncertainties in the dynamic model can disrupt forward in…
Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control
Omkar Sudhir Patil, Duc M. Le, Emily J. Griffis +1
Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the adaptiv…
Simultaneous Online System Identification and Control using Composite Adaptive Lyapunov-Based Deep Neural Networks
Omkar Sudhir Patil, Emily J. Griffis, Wanjiku A. Makumi +1
Although deep neural network (DNN)-based controllers are popularly used to control uncertain nonlinear dynamic systems, most results use DNNs that are pretrained offline and the co…
Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller
Saiedeh Akbari, Emily J. Griffis, Omkar Sudhir Patil +1
Deep neural network (DNN)-based adaptive controllers can be used to compensate for unstructured uncertainties in nonlinear dynamic systems. However, DNNs are also very susceptible…