RM-WAM: Reliable Memory-Augmented World Action Models for Long-Horizon Robot Manipulation
cs.RO
Submitted: 2026-10-11
Updated: 2026-10-15
Sources
- OpenVLA: An Open-Source Vision-Language-Action Model
- Octo: An Open-Source Generalist Robot Policy
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
- Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- GigaWorld-Policy: An Efficient Action-Centered World--Action Model
- World Action Models are Zero-shot Policies
- Fast-WAM: Do World Action Models Need Test-time Future Imagination?
- MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
- MEM: Multi-Scale Embodied Memory for Vision Language Action Models
- ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries
- Mem-$\pi$: Adaptive Memory through Learning When and What to Generate
- RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
- UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
- GigaBrain-0: A World Model-Powered Vision-Language-Action Model
- ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context
- SmolVLM: Redefining small and efficient multimodal models
- Qwen3-VL Technical Report
- RT-1: Robotics Transformer for Real-World Control at Scale
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies