1 citations · 5 across the 16 of their papers we have counts for
19 papers
Akule: Fast and Scalable Multi-Robot Motion Planning via Sparse Interaction Diffusion
Manav Vora, Gokul Puthumanaillam, Aditya Penumarti +2
Diffusion models provide expressive trajectory priors for multi-robot motion planning (MRMP), but scaling them to large teams requires coordinating individual motions without expen…
When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs
Negin Musavi, Gokul Puthumanaillam, Ruben Hernandez +2
Robots operating in non-stationary environments must continually adapt their policies as the dynamics drift, but onboard energy and compute budgets cap how often a full state estim…
Task-Aware Environment Augmentation for Reliable Navigation via Shielded Conditional Diffusion
Bharawee Phoompho, Gokul Puthumanaillam, Yan Miao +4
Reliable trajectory planning under partial observability depends not only on computing a feasible geometric path, but also on whether the robot receives informative observations wh…
Trajectory-Level Redirection Attacks on Vision-Language-Action Models
Gokul Puthumanaillam, Vardhan Dongre, Pranay Thangeda +3
Vision-language-action (VLA) policies bring natural language into closed-loop robot control, enabling robots to execute manipulation tasks directly from text instructions. The same…
Viscosity-Informed Generative Actor-Critic for High-Dimensional Stochastic Optimal Control
Alen E. Golpashin, Gokul Puthumanaillam, Melkior Ornik +1
We introduce a method for approximating viscosity solutions of stationary degenerate elliptic Hamilton--Jacobi--Bellman equations on bounded domains arising in stochastic exit-time…
Muninn: Your Trajectory Diffusion Model But Faster
Gokul Puthumanaillam, Hao Jiang, Ruben Hernandez +4
Diffusion-based trajectory planners can synthesize rich, multimodal robot motions, but their iterative denoising makes online planning and control prohibitively slow. Existing acce…
Amortizing Trajectory Diffusion with Keyed Drift Fields
Gokul Puthumanaillam, Melkior Ornik
Diffusion-based trajectory planners can synthesize rich, multimodal action sequences for offline reinforcement learning, but their iterative denoising incurs substantial inference-…
SCoUT: Scalable Communication via Utility-Guided Temporal Grouping in Multi-Agent Reinforcement Learning
Manav Vora, Gokul Puthumanaillam, Hiroyasu Tsukamoto +1
Communication can improve coordination in partially observed multi-agent reinforcement learning (MARL), but learning \emph{when} and \emph{who} to communicate with requires choosin…
Online Learning of Deceptive Policies under Intermittent Observation
Gokul Puthumanaillam, Ram Padmanabhan, Jose Fuentes +7
In supervisory control settings, autonomous systems are not monitored continuously. Instead, monitoring often occurs at sporadic intervals within known bounds. We study the problem…
Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing
Gokul Puthumanaillam, Aditya Penumarti, Manav Vora +5
Robots equipped with rich sensor suites can localize reliably in partially-observable environments, but powering every sensor continuously is wasteful and often infeasible. Belief-…
Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements
Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes +2
Robots navigating complex environments must manage uncertainty from sensor noise, environmental changes, and incomplete information, with different tasks requiring varying levels o…
Motion Planning and Control with Unknown Nonlinear Dynamics through Predicted Reachability
Zhiquan Zhang, Gokul Puthumanaillam, Manav Vora +1
Autonomous motion planning under unknown nonlinear dynamics presents significant challenges. An agent needs to continuously explore the system dynamics to acquire its properties, s…
TRACE: A Self-Improving Framework for Robot Behavior Forecasting with Vision-Language Models
Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes +6
Predicting the near-term behavior of a reactive agent is crucial in many robotic scenarios, yet remains challenging when observations of that agent are sparse or intermittent. Visi…
The Lazy Student's Dream: ChatGPT Passing an Engineering Course on Its Own
Gokul Puthumanaillam, Timothy Bretl, Melkior Ornik
This paper presents a comprehensive investigation into the capability of Large Language Models (LLMs) to successfully complete a semester-long undergraduate control systems course.…
TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning
Gokul Puthumanaillam, Jae Hyuk Song, Nurzhan Yesmagambet +2
Autonomous agents operating in adversarial scenarios face a fundamental challenge: while they may know their adversaries' high-level objectives, such as reaching specific destinati…
GUIDEd Agents: Enhancing Navigation Policies through Task-Specific Uncertainty Abstraction in Localization-Limited Environments
Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes +2
Autonomous vehicles performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation. In many scenarios, such as stealth opera…
A Moral Imperative: The Need for Continual Superalignment of Large Language Models
Gokul Puthumanaillam, Manav Vora, Pranay Thangeda +1
This paper examines the challenges associated with achieving life-long superalignment in AI systems, particularly large language models (LLMs). Superalignment is a theoretical fram…
ComTraQ-MPC: Meta-Trained DQN-MPC Integration for Trajectory Tracking with Limited Active Localization Updates
Gokul Puthumanaillam, Manav Vora, Melkior Ornik
Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates -- the process by which the agent o…
Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment
Gokul Puthumanaillam, Xiangyu Liu, Negar Mehr +1
Optimal decision-making presents a significant challenge for autonomous systems operating in uncertain, stochastic and time-varying environments. Environmental variability over tim…