1 citations · 1 across the 7 of their papers we have counts for
7 papers
Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations
Xinglong Zhang, Cong Li, Hangjie Mo +13
Soft-bodied organisms such as octopuses and elephant trunks exhibit remarkable morphological adaptability, dynamically reconfiguring body shape and stiffness, and flexibly adjustin…
CoINS: Counterfactual Interactive Navigation via Skill-Aware VLM
Kangjie Zhou, Zhejia Wen, Zhiyong Zhuo +9
Recent Vision-Language Models (VLMs) have demonstrated significant potential in robotic planning. However, they typically function as semantic reasoners, lacking an intrinsic under…
DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation
Shuyuan Wang, Philip D. Loewen, Michael Forbes +2
While differentiable control has emerged as a powerful paradigm combining model-free flexibility with model-based efficiency, the iterative Linear Quadratic Regulator (iLQR) remain…
Computation-Aware Learning for Stable Control with Gaussian Process
Wenhan Cao, Alexandre Capone, Rishabh Yadav +2
In Gaussian Process (GP) dynamical model learning for robot control, particularly for systems constrained by computational resources like small quadrotors equipped with low-end pro…
Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control
Wenhan Cao, Wei Pan
Integral reinforcement learning (IntRL) demands the precise computation of the utility function's integral at its policy evaluation (PEV) stage. This is achieved through quadrature…
Analyzing the Impact of Computation in Adaptive Dynamic Programming for Stochastic LQR Problem
Wenhan Cao, Alexandre Capone, Sandra Hirche +1
Adaptive dynamic programming (ADP) for stochastic linear quadratic regulation (LQR) demands the precise computation of stochastic integrals during policy iteration (PI). In a fully…
Online Optimization with Feedback Delay and Nonlinear Switching Cost
Weici Pan, Guanya Shi, Yiheng Lin +1
We study a variant of online optimization in which the learner receives -round about hitting cost and there is a multi-step nonlinear switching cost,…