Grounding Language Models in Autonomous Loco-manipulation Tasks
<|reference_start|>Grounding Language Models in Autonomous Loco-manipulation Tasks: Humanoid robots with behavioral autonomy have consistently been regarded as ideal collaborators in our daily lives and promising representations of embodied intelligence. Compared to fixed-based robotic arms, humanoid robots offer a larger operational space while significantly increasing the difficulty of control and planning. Despite the rapid progress towards general-purpose humanoid robots, most studies remain focused on locomotion ability with few investigations into whole-body coordination and tasks planning, thus limiting the potential to demonstrate long-horizon tasks involving both mobility and manipulation under open-ended verbal instructions. In this work, we propose a novel framework that learns, selects, and plans behaviors based on tasks in different scenarios. We combine reinforcement learning (RL) with whole-body optimization to generate robot motions and store them into a motion library. We further leverage the planning and reasoning features of the large language model (LLM), constructing a hierarchical task graph that comprises a series of motion primitives to bridge lower-level execution with higher-level planning. Experiments in simulation and real-world using the CENTAURO robot show that the language model based planner can efficiently adapt to new loco-manipulation tasks, demonstrating high autonomy from free-text commands in unstructured scenes.<|reference_end|>
@article{wang2024grounding,
    title={Grounding Language Models in Autonomous Loco-manipulation Tasks},
    author={Jin Wang, Nikos Tsagarakis},
    journal={arXiv preprint arXiv:2409.01326},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01326},
    primaryClass={cs.RO cs.AI cs.LG}
}