37 citations · 151 across the 85 of their papers we have counts for
28 papers · 1 filter
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…
Fast Direction-Conditioned Reachability for Motion Prediction Under Model Uncertainty
Hrishav Das, Melkior Ornik
To avoid collisions, a robot must repeatedly predict where nearby agents may move, usually with an imperfect model of their dynamics. Reachable sets provide such predictions, but c…
Risk-Aware Motion Planning and Control under Unknown Dynamics with Hybrid Observations
Zhiquan Zhang, Melkior Ornik
We consider robotic motion planning and control under unknown dynamics with hybrid state observations, where state measurements are available only in parts of the state space. Exis…
Safe, Real-Time Active Model Discrimination and Fault Diagnosis for Nonlinear Systems via Differentiable Reachability
Xinpei Ni, Melkior Ornik, Glen Chou +1
We present a safe, real-time algorithm for active fault diagnosis and model discrimination for uncertain continuous-time nonlinear systems with process and measurement disturbances…
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…
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…
Hierarchical Motion Planning and Control under Unknown Nonlinear Dynamics via Predicted Reachability
Zhiquan Zhang, Melkior Ornik
Autonomous motion planning under unknown nonlinear dynamics requires learning system properties while navigating toward a target. In this work, we develop a hierarchical planning-c…
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-…
Navigating in Uncertain Environments with Heterogeneous Visibility
Jongann Lee, Melkior Ornik
Navigating an environment with uncertain connectivity requires a strategic balance between minimizing the cost of traversal and seeking information to resolve map ambiguities. Unli…
Reachable Predictive Control: A Novel Control Algorithm for Nonlinear Systems with Unknown Dynamics and its Practical Applications
Taha Shafa, Yiming Meng, Melkior Ornik
This paper proposes an algorithm capable of driving a system to follow a piecewise linear trajectory without prior knowledge of the system dynamics. Motivated by a critical failure…
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…
Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing
Neeloy Chakraborty, John Pohovey, Melkior Ornik +1
Large language models (LLMs) have recently demonstrated success in decision-making tasks including planning, control, and prediction, but their tendency to hallucinate unsafe and u…
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…
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…
Autonomous Excavation of Challenging Terrain using Oscillatory Primitives and Adaptive Impedance Control
Noah Franceschini, Pranay Thangeda, Melkior Ornik +1
This paper addresses the challenge of autonomous excavation of challenging terrains, in particular those that are prone to jamming and inter-particle adhesion when tackled by a sta…
Learning to Turn: Diffusion Imitation for Robust Row Turning in Under-Canopy Robots
Arun N. Sivakumar, Pranay Thangeda, Yixiao Fang +4
Under-canopy agricultural robots require robust navigation capabilities to enable full autonomy but struggle with tight row turning between crop rows due to degraded GPS reception,…
Few-shot Scooping Under Domain Shift via Simulated Maximal Deployment Gaps
Yifan Zhu, Pranay Thangeda, Erica L Tevere +5
Autonomous lander missions on extraterrestrial bodies need to sample granular materials while coping with domain shifts, even when sampling strategies are extensively tuned on Eart…
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…