UniWAM Technical Report: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision

arXiv:2609.39388 · cs.RO, cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/Fysics-AI/UniWAM

Project page: https://fysics-ai.github.io/uniwam.github.io

Terminology

• Transformer — 11× in this paper · explained in 2 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Action Space — 10× in this paper · explained in 1 episode
• Manipulation Anchor Pose — 10× in this paper
• Affordance — 8× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Foundation Model — 6× in this paper · explained in 3 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• World Model — 5× in this paper · explained in 7 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Hysteresis — 2× in this paper · explained in 3 episodes
• VLA Models — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Motion Primitives — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• Motus: A Unified Latent Action World Model
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout
• GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning
• ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
• MobileWAM: Bridging World Action Models to Mobile Manipulation with Chain-of-Foresight
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• Causal World Modeling for Robot Control
• Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
• DINOv2: Learning Robust Visual Features without Supervision
• mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
• ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
• SG-VLA: Learning Spatially-Grounded Vision-Language-Action Models for Mobile Manipulation
• Wan: Open and Advanced Large-Scale Video Generative Models
• MoTo: A Zero-shot Plug-in Interaction-aware Navigation for General Mobile Manipulation
• Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
• Habitat-Matterport 3D Semantics Dataset
• Learning Panorama-Aware VLA for Mobile Manipulation with Whole-Body Teleoperation
• Mobi-$\pi$: Mobilizing Your Robot Learning Policy

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• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving