Teach and Grow: An Agent-Centered Architecture for General Robot Learning
cs.RO, cs.AI, cs.CV, cs.LG
Submitted: 2026-08-17
Updated: 2026-09-20
Comments: Accepted by The International Journal of Robotics Research (IJRR 2026). Project page: https://hear.irmv.top
DOI: 10.1177/02783649261477778
Project page: https://hear.irmv.top
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- RT-1: Robotics Transformer for Real-World Control at Scale
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- Octo: An Open-Source Generalist Robot Policy
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- World Action Models are Zero-shot Policies
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
- RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation
- AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
- From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models
- ASPIRE: Agentic /Skills Discovery for Robotics
- Playful Agentic Robot Learning
- Scaling Laws for Neural Language Models
- Training Compute-Optimal Large Language Models
- Neural Scaling Laws in Robotics
- Data Scaling Laws in Imitation Learning for Robotic Manipulation
- Guiding Data Collection via Factored Scaling Curves
- VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
- SkillPlug: Unsupervised Skill Mining for Few-Shot Adaptation in Robotic Manipulation
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