Towards Agentic Robots: Model, Data, Environment, and Harness
cs.RO
Submitted: 2026-10-14
Updated: 2026-10-14
Code: https://github.com/chonghanqin/Awesome-Agentic-Robots
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Efficient Vision-Language-Action Management and Serving for Robot Factories
- GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
- AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- Awomo-SimDataEngine: Agentic Simulation-ReadyWorld Generation
- Action Chunk Scheduling for Batched Robot Policy Serving
- LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation
- RT-H: Action Hierarchies Using Language
- MG-VQA: Manipulation Grounded Visual Question Answering with VLMs
- Motus: A Unified Latent Action World Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- Real-Time Execution of Action Chunking Flow Policies
- Training-Time Action Conditioning for Efficient Real-Time Chunking
- SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- RT-1: Robotics Transformer for Real-World Control at Scale
- Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation
- Seed1.8 Model Card: Towards Generalized Real-World Agency
- LeRobot: An Open-Source Library for End-to-End Robot Learning
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