4 citations · 5 across the 7 of their papers we have counts for
9 papers
SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection
Tong Jian, Aditya Thurvas Senthil Kumar, Xinyi Li +7
Slip detection is fundamental to dexterous manipulation, yet existing systems often lack precise characterization of detection latency and cross-platform generalization. We present…
Learning Nonlinear Systems In-Context: From Synthetic Data to Real-World Motor Control
Tong Jian, Tianyu Dai, Tao Yu
LLMs have shown strong in-context learning (ICL) abilities, but have not yet been extended to signal processing systems. Inspired by their design, we have proposed for the first ti…
Automated Creation of Digital Cousins for Robust Policy Learning
Tianyuan Dai, Josiah Wong, Yunfan Jiang +5
Training robot policies in the real world can be unsafe, costly, and difficult to scale. Simulation serves as an inexpensive and potentially limitless source of training data, but…
Deep Learning of Dynamic Systems using System Identification Toolbox(TM)
Tianyu Dai, Khaled Aljanaideh, Rong Chen +3
MATLAB(R) releases over the last 3 years have witnessed a continuing growth in the dynamic modeling capabilities offered by the System Identification Toolbox(TM). The emphasis has…
Data-Driven Superstabilizing Control under Quadratically-Bounded Errors-in-Variables Noise
Jared Miller, Tianyu Dai, Mario Sznaier
The Error-in-Variables model of system identification/control involves nontrivial input and measurement corruption of observed data, resulting in generically nonconvex optimization…
Data-Driven Control of Positive Linear Systems using Linear Programming
Jared Miller, Tianyu Dai, Mario Sznaier +1
This paper presents a linear-programming based algorithm to perform data-driven stabilizing control of linear positive systems. A set of state-input-transition observations is coll…
Robust Data-Driven Safe Control using Density Functions
Jian Zheng, Tianyu Dai, Jared Miller +1
This paper presents a tractable framework for data-driven synthesis of robustly safe control laws. Given noisy experimental data and some priors about the structure of the system,…
Superstabilizing Control of Discrete-Time ARX Models under Error in Variables
Jared Miller, Tianyu Dai, Mario Sznaier
This paper applies a polynomial optimization based framework towards the superstabilizing control of an Autoregressive with Exogenous Input (ARX) model given noisy data observation…
Data-Driven Stabilizing and Robust Control of Discrete-Time Linear Systems with Error in Variables
Jared Miller, Tianyu Dai, Mario Sznaier
This work presents a sum-of-squares (SOS) based framework to perform data-driven stabilization and robust control tasks on discrete-time linear systems where the full-state observa…