9 papers
Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoning
Taozhao Chen, Ian Manchester, Huaming Chen
Vision-Language-Action (VLA) systems, built on pretrained vision-language models (VLMs), have shown rapidly improving performance on robot manipulation benchmarks. These gains are…
Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
Dechuan Liu, Ruigang Wang, Ian R. Manchester
This paper presents a learning-based approach for all-pairs motion planning, where the initial and goal states are allowed to be arbitrary points in a safe set. We construct smooth…
Remarks on Lipschitz-Minimal Interpolation: Generalization Bounds and Neural Network Implementation
Arthur C. B. de Oliveira, Ruigang Wang, Ian R. Manchester +1
This note establishes a theoretical framework for finding (potentially overparameterized) approximations of a function on a compact set with a-priori bounds for the generalization…
Constraining Streaming Flow Models for Adapting Learned Robot Trajectory Distributions
Jieting Long, Dechuan Liu, Weidong Cai +2
Robot motion distributions often exhibit multi-modality and require flexible generative models for accurate representation. Streaming Flow Policies (SFPs) have recently emerged as…
Trust in LLM-controlled Robotics: a Survey of Security Threats, Defenses and Challenges
Xinyu Huang, Shyam Karthick V B, Taozhao Chen +5
The integration of Large Language Models (LLMs) into robotics has revolutionized their ability to interpret complex human commands and execute sophisticated tasks. However, such pa…
EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
Raghav Mishra, Ian R. Manchester
We propose enforcing constraints on Model-Based Diffusion by introducing emerging barrier functions inspired by interior point methods. We demonstrate that the standard Model-Based…