2 citations · 2 across the 19 of their papers we have counts for
10 papers · 1 filter
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…