10 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…
Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
Andrea Martin, Ian R. Manchester, Luca Furieri
The design of many classical optimization algorithms is driven by the certification of linear convergence rates over classes of optimization problems. In this paper, we consider th…
Training-Free Imitation Learning with Closed-Form Diffusion Policies
Raghav Mishra, Ian R. Manchester
While diffusion-based policies have impressive performance and expressivity, their long offline training slows down the data collection and policy deployment loop. We introduce Clo…
DynoJEPP: Joint Estimation, Prediction and Planning in Dynamic Environments
Mikolaj Kliniewski, Jesse Morris, Yiduo Wang +2
DynoJEPP is a factor-graph-based framework that jointly formulates and simultaneously optimizes estimation, prediction, and planning in dynamic environments. In conventional factor…
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…