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] BrickCraft: Visuomotor Skill Composition with Situated Manual Guidance for Long-Horizon Interlocking Brick Assembly
   Jichuan Yu, Bowei Li, Zhenran Tang, Guanxing Lu, Chuxiong Hu, Ruixuan Liu and Changliu Liu
   arXiv:2605.07605, 2026
   

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   Autonomous robotic assembly of interlocking bricks demands seamless integration of long-horizon task reasoning, spatial grounding, and fine-grained manipulation. This paper presents BrickCraft, a compositional framework designed for long-horizon and generalizable interlocking brick assembly. BrickCraft models the assembly process using a relative formulation, where each step is anchored to a reference brick within the partial structure, thereby decomposing complex tasks into a finite set of reusable primitive skills. BrickCraft bridges the gap between high-level assembly plans and physical execution through situated manuals, which provide explicit spatial guidance for learned visuomotor skills by projecting the assembly intent onto real-time robot observations. Finally, BrickCraft employs a compositional execution pipeline that chains these spatially grounded skills to accomplish long-horizon assembly tasks. Extensive experimental validations demonstrate that BrickCraft acquires proficient assembly skills from a limited set of demonstrations and exhibits strong compositional generalization to unseen structures.

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2. [U] ManiSkillFormer: Demonstration-Free Compositional Manipulation via Task-Conditioned Geometric Contracts
   Peiqi Yu, Mosam Dabhi, Shangtao Li, Bowei Li, Laszlo Jeni and Changliu Liu
   arXiv:2609.16331, 2026
   

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   Adapting robotic manipulation to new objects and tasks often requires additional demonstrations, policy fine-tuning, or manual engineering. Reusable manipulation skills can reduce this effort, but connecting their execution requirements to scene-specific geometry remains challenging. We present ManiSkillFormer, a framework for demonstration-free and compositional manipulation that connects perception and action through explicit geometric contracts. Building on reusable skill schemas, LLM agents generate contracts specifying the geometry primitives required by each skill, together with corresponding motion templates for semantic objects and task contexts. These contracts guide a perception module to ground task-relevant 3D geometry from observations, which is then used to instantiate reusable motion templates in a skill library. We evaluate ManiSkillFormer on a dual-arm robot across demonstration-free pick-and-place with 30 instances from 8 object categories, functional manipulation including unscrewing, pouring, pressing, and folding, and three long-horizon tasks. ManiSkillFormer achieves an average success rate of 88.97% for pick-and-place, 75.00% for functional manipulation, and completion rates of 50–80% across the long-horizon tasks, outperforming the evaluated baselines and two ablated pipelines. These results demonstrate the potential of explicit geometric contracts to support skill reuse and composition across objects and tasks without per-object policy fine-tuning or additional robot demonstrations.

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3. [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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