15 papers
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-…