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This paper introduces a nested kino-dynamic framework that enhances the rapid feasibility checking and trajectory generation for humanoid loco-manipulation in robotics. By combining this framework with a feasibility-guided tree search and an LLM-based contact plan sampling strategy, the authors significantly improve the efficiency of the search process for viable motion plans. The resulting trajectories are shown to be executable in real-world scenarios through a reinforcement learning-based controller, demonstrating both their quality and practicality.
The integration of LLMs into robot motion planning leads to a dramatic improvement in the efficiency of generating feasible trajectories for complex loco-manipulation tasks.
Fast planning of novel behaviors in unseen scenarios remains a fundamental challenge in robotics. The high-dimensional, hybrid, and underactuated nature of humanoid loco-manipulation continues to hinder the realization of this goal. In this paper, we address this challenge by proposing a nested kino-dynamic framework for rapid feasibility checking and dynamically consistent trajectory generation given a candidate contact sequence. By integrating this module with a feasibility-guided tree search and a Large Language Model (LLM)-based contact plan sampling strategy, we demonstrate that the proposed framework can substantially improve the search process. Furthermore, we show that the generated trajectories can be tracked using a reinforcement learning (RL)-based controller and show that the resulting trajectories are of sufficiently high quality for execution in real-world loco-manipulation scenarios. A supplementary video is available at: https://youtu.be/R6qCHoCormQ.
