In this paper, we propose a novel framework for synthesizing a single multimodal control policy capable of generating diverse behaviors (or modes) and emergent inherent transition maneuvers for bipedal locomotion. In our method, we first learn efficient latent encodings for each behavior by training an autoencoder from a dataset of rough reference motions. These latent encodings are used as commands to train a multimodal policy through an adaptive sampling of modes and transitions to ensure consistent performance across different behaviors. We validate the policy performance in simulation for various distinct locomotion modes such as walking, leaping, jumping on a block, standing idle, and all possible combinations of inter-mode transitions. Finally, we integrate a task-based planner to rapidly generate open-loop mode plans for the trained multimodal policy to solve high-level tasks like reaching a goal position on a challenging terrain. Complex parkour-like motions by smoothly combining the discrete locomotion modes were generated in 3 min. to traverse tracks with a gap of width 0.45 m, a plateau of height 0.2 m, and a block of height 0.4 m, which are all significant compared to the dimensions of our mini-biped platform.

翻译：本文提出了一种新颖框架，用于合成单一的多模态控制策略，该策略能够生成双足运动的多种行为（或模态）及突现的固有过渡机动。方法中，我们首先通过从粗糙参考运动数据集中训练自编码器，为每种行为学习高效的潜在编码。这些潜在编码作为指令，通过自适应采样模态与过渡来训练多模态策略，以确保不同行为间性能的一致性。我们在仿真中验证了策略在多种不同运动模态下的表现，例如行走、跳跃、跳上障碍物、静止站立以及所有可能的模态间过渡组合。最后，我们集成了一种基于任务的规划器，为训练好的多模态策略快速生成开环模态计划，以解决高层任务，如在复杂地形上到达目标位置。通过平滑组合离散运动模态，我们在3分钟内生成了复杂的跑酷式运动，用于穿越宽0.45米间隙、高0.2米平台及高0.4米障碍物的轨道——这些尺寸相对于我们微型双足平台均具有显著挑战性。