loco-manipulation

InterEvolve introduces test-time evolution of reward programs for humanoid loco-manipulation, using an object-aware forward-backward behavioral foundation model and an LLM agent that revises staged reward programs in-context to unlock untrained skills, with evolved behaviors deployed autonomously on a physical Unitree G1 robot.

OTRetarget 提出一种统一方法，利用熵最优传输联合重定向机器人与多物体的运动，通过保持接触一致性显著优于 OmniRetarget，并在物理人形机器人 G1 上验证了迁移到强化学习训练的全身策略的可行性。

PRISM is a real-to-sim-to-real framework that amplifies a few real human-object interaction videos into hundreds of diverse counterfactual videos via V2V generation, then reconstructs physically plausible motions to train a generalizable humanoid loco-manipulation policy deployed on a real robot without real-world fine-tuning.

FetchMan is a vision-based humanoid policy trained entirely in simulation, capable of zero-shot transfer to diverse real-world scenes and objects for loco-manipulation tasks.

This paper introduces LUCID, a hierarchical model-based reinforcement learning framework for long-horizon humanoid loco-manipulation. It learns reusable latent skills and a macro-dynamics world model, enabling high-level planning via imagined rollouts and improving success rates in simulated multi-object rearrangement tasks.

OASIS is a simulation-data-driven framework for humanoid loco-manipulation that uses 3D generative models and hierarchical visuomotor policies. It achieves better zero-shot performance than real-robot training by leveraging domain randomization in simulation.

GRAIL generates diverse humanoid manipulation and locomotion data using 3D assets and video foundation models, enabling effective sim-to-real transfer for humanoid robot control with high real-world success rates.