Grounding Language Models in Autonomous Loco-manipulation Tasks

• URL: http://arxiv.org/abs/2409.01326v1
• Date: Mon, 2 Sep 2024 15:27:48 GMT
• Title: Grounding Language Models in Autonomous Loco-manipulation Tasks
• Authors: Jin Wang, Nikos Tsagarakis,
• Abstract summary: We propose a novel framework that learns, selects, and plans behaviors based on tasks in different scenarios. We leverage the planning and reasoning features of the large language model (LLM), constructing a hierarchical task graph. 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.
• Score: 3.8363685417355557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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