2026

1. [C116] Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling
   Zhilin He, Bowei Li, Jianlin Dou, Yuner Zhang and Changliu Liu
   IEEE/RSJ International Conference on Intelligent Robots and Systems, 2026
   

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   Single-Robot Motion Planning (SRMP) in highly non-convex constrained environments, where robots must satisfy collision-free guarantees, dynamic feasibility, and task-related constraints, is challenging under complex constraints and computational limits. Recent Model-Based Diffusion (MBD) approaches recast the SRMP as trajectory optimization that samples from a posterior over trajectories, using known dynamics, and analytically estimates the score function from rollout samples to guide diffusion denoising toward a low-cost, clean trajectory without demonstration learning. While existing works further adapt MBD to constrained environments and showcase promising performance, they are still limited by (1) enforcing safety either via soft feasibility diffusion priors or hard projection operators, but lack a unified framework to integrate both, and (2) fixing safety enforcement to neglect the changing of diffusion scheduling. Therefore, we introduce 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, along with diffusion scheduling. Experiments demonstrate that our method achieves higher safety & success rates, faster convergence, and lower final costs than baseline planners on randomly generated highly non-convex 2D benchmarks and a 7-DoF robot arm avoidance task.

1. [U] ARSTAG: An Agentic Real2Sim2Real System for Task-Specific Robot Data Generation
   Bowei Li, Yuner Zhang and Changliu Liu
   arXiv:2609.24563, 2026
   

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   ARSTAG automates the creation of robot policy training data from one RGB image and a language instruction. Coordinated language agents reconstruct task-relevant simulation scenes, produce feasible robot demonstrations, and diversify the data using task-consistent randomization. Evaluation covers seven manipulation tasks and three visuomotor policy architectures on a dual-arm robot. The pi0.5 policy achieves 74.6 percent average real-world success. Ablations indicate improved robustness from randomization and better performance with larger generated datasets.

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