ME-Brain-1.0: Memory, Cognition and Action for Evolving Embodied Intelligence

arXiv:2609.24271 · cs.RO · Submitted 2026-09-21 · Read on arXiv

cs.RO

Submitted: 2026-09-21

Updated: 2026-09-29

Code: https://github.com/MachEmbodied/ME-Brain-1.0

Project page: https://machembodied.com/ME-Brain/ME-Brain-1.0.html

Terminology

• Core — 41× in this paper · explained in 2 episodes
• Benchmark — 26× in this paper · explained in 2 episodes
• Task Planning — 12× in this paper · explained in 2 episodes
• Generalization — 9× in this paper · explained in 13 episodes
• Trace — 9× in this paper · explained in 2 episodes
• Verification — 9× in this paper · explained in 2 episodes
• Abstraction — 7× in this paper · explained in 1 episode
• Success Rate — 7× in this paper
• Redundancy — 6× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Affordance — 4× in this paper · explained in 1 episode
• Embodied AI — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Edge Deployment — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Vision-Language-Action (VLA) Models — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Reasoning — 1× in this paper · explained in 2 episodes
• Agent Memory — 1× in this paper · explained in 1 episode
• Agentic Architecture — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Agentic Loop — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Procedural Knowledge — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• State Aliasing — 1× in this paper · explained in 2 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• PaliGemma: A versatile 3B VLM for transfer
• OpenVLA: An Open-Source Vision-Language-Action Model
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution
• ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI
• EmbodiSkill: Skill-Aware Reflection for Self-Evolving Embodied Agents
• Robots Acquire Manipulation Skills in Seconds from a Single Human Video
• Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
• MemOS: A Memory OS for AI System
• MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
• MemER: Scaling Up Memory for Robot Control via Experience Retrieval
• MEM: Multi-Scale Embodied Memory for Vision Language Action Models
• RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks
• World Action Models are Zero-shot Policies
• GigaWorld-Policy: An Efficient Action-Centered World--Action Model
• EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies
• Infrastructure First: Enabling Embodied AI for Science in the Global South
• Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI

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• 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