Researchers propose a real-to-sim-to-real framework enabling rapid test-time fine-tuning of humanoid parkour policies on unfamiliar terrains, using mesh reconstruction from real-world captures. The two-stage…

#Robotics #HumanoidRobots #SimToReal #TestTimeTraining
https://arxiv.org/abs/2602.02331

A curriculum-driven framework pairs Evolution Strategy policy learning with Statistical Model Checking to co-optimize performance and enlarge the safe operating region for sim-to-real robotic controllers. The approach…

#Robotics #SimToReal #NeuralControl #SafeRL
https://arxiv.org/abs/2608.06481

Researchers propose a Real-to-Sim-to-Real framework with an information-theoretic cost function to reduce the sim-to-real gap, integrating with RL algorithms like PPO and SAC and optionally using differentiable simulators.

#Robotics #SimToReal #ReinforcementLearning
https://arxiv.org/abs/2503.10118

New arXiv work shows vision-language-action models can be reinforcement-learning fine-tuned across diverse simulated scenes using generative 3D worlds, avoiding costly sim-to-real fixes and scene-specific…

#robotics #AI #simtoreal #reinforcementlearning
https://arxiv.org/abs/2603.18532

ExpertGen automates expert policy learning in simulation by refining diffusion-based behavior priors from imperfect demos, including LLM-synthesized ones, using reinforcement learning while keeping the…

#Robotics #SimToReal #DiffusionPolicy #BehaviorCloning
https://arxiv.org/abs/2603.15956