activity
20152026
most citedSome Results on an Affine Obstruction to Reach Control

4 citations · 4 across the 23 of their papers we have counts for

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16 papers · 1 filter

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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-…