Sim-to-Real Aware End-to-End Learning Environment for Micromobility

arXiv:2609.28969 · cs.RO · Submitted 2026-09-24 · Read on arXiv

cs.RO

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• World Model — 16× in this paper · explained in 7 episodes
• Sim-to-Real Gap — 11× in this paper · explained in 6 episodes
• Sim-to-Real Transfer — 9× in this paper · explained in 2 episodes
• System Identification — 8× in this paper · explained in 3 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Core — 5× in this paper · explained in 2 episodes
• Bayesian Optimization — 2× in this paper · explained in 3 episodes
• Conference on Robot Learning — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Digital Twin — 1× in this paper · explained in 5 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• PID Controller — 1× in this paper · explained in 2 episodes
• Point Cloud Data — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• EMMA: End-to-End Multimodal Model for Autonomous Driving
• A Survey of World Models for Autonomous Driving
• GAIA-1: A Generative World Model for Autonomous Driving
• Mastering Diverse Domains through World Models
• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility
• Follow-Me in Micro-Mobility with End-to-End Imitation Learning
• Sim-to-Real Transfer for Mobile Robots with Reinforcement Learning: from NVIDIA Isaac Sim to Gazebo and Real ROS 2 Robots

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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