Constrained Locomotion Planning (CLP) for quadrupeds and humanoids, where robots must satisfy collision avoidance, contact consistency, kinematic feasibility, and support constraints, is challenging under high-dimensional dynamics and highly non-convex environments. Recent Model-Based Diffusion (MBD) approaches recast...
FedGuide is proposed, a FRL framework that uses diffusion priors as behavior models to provide personalized data supported distributions for heterogeneous local policy learning and develops a Distribution Correction Estimation value baseline to provide low-variance, return-aware guidance for local policy improvement.
The study shows that the improvement of opposition-based learning plays the most significant role in all three mechanisms and shows an efficient multi-UAV path planning for urban aerial vehicles that outperform current solutions and can be readily integrated into learning-based approaches and deployed on physical hardw...
This work introduces Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling (MD-COAS) for SRMP that unifies the inexact Augmented Lagrangian Method (iALM) soft diffusion prior with a Convex Feasible Set (CFS)-based hard projection operator, and adaptively schedules and co-optimizes safety enforcement...
This work introduces Model-Based Diffusion Optimal Control (MDOC), a model-based diffusion planner that efficiently produces dynamically feasible trajectories without relying on data, and shows that MDOC's safety mechanism naturally scales to multi-robot planning settings through Conflict-Based Search.
