1 citations · 1 across the 5 of their papers we have counts for
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Function-Space Diffusion for Motion Planning
Zinuo Chang, Yipu Chen, Byoungwoo Park +2
Diffusion-based motion planners have demonstrated strong performance in generating diverse and high-quality robot trajectories in cluttered environments with multiple feasible solu…
PISTO: Proximal Inference for Stochastic Trajectory Optimization
Hongzhe Yu, Zinuo Chang, Yongxin Chen
Stochastic trajectory optimization methods like STOMP enable planning with non-differentiable costs, offering substantial flexibility over gradient-based approaches. We show that S…
Path Integral Particle Filtering for Hybrid Systems via Saltation Matrices
Karthik Shaji, Sreeranj Jayadevan, Bo Yuan +2
State estimation for hybrid systems that undergo intermittent contact with their environments, such as extraplanetary robots and satellites undergoing docking operations, is diffic…
Efficient Iterative Proximal Variational Inference Motion Planning
Zinuo Chang, Hongzhe Yu, Patricio Vela +1
We cast motion planning under uncertainty as a stochastic optimal control problem, where the optimal posterior distribution has an explicit form. To approximate this posterior, thi…
Path Integral Control for Hybrid Dynamical Systems
Hongzhe Yu, Diana Frias Franco, Aaron M. Johnson +1
This work introduces a novel paradigm for solving optimal control problems for hybrid dynamical systems under uncertainties. Robotic systems having contact with the environment can…
Stochastic Motion Planning as Gaussian Variational Inference: Theory and Algorithms
Hongzhe Yu, Yongxin Chen
We present a novel formulation for motion planning under uncertainties based on variational inference where the optimal motion plan is modeled as a posterior distribution. We propo…