Dexterous robotic manipulation remains a significant challenge due to the high dimensionality and complexity of hand movements required for tasks like in-hand manipulation and object grasping. This paper addresses this issue by introducing Vector Quantized Action Chunking Embedding (VQ-ACE), a novel framework that compresses human hand motion into a quantized latent space, significantly reducing the action space's dimensionality while preserving key motion characteristics. By integrating VQ-ACE with both Model Predictive Control (MPC) and Reinforcement Learning (RL), we enable more efficient exploration and policy learning in dexterous manipulation tasks using a biomimetic robotic hand. Our results show that latent space sampling with MPC produces more human-like behavior in tasks such as Ball Rolling and Object Picking, leading to higher task success rates and reduced control costs. For RL, action chunking accelerates learning and improves exploration, demonstrated through faster convergence in tasks like cube stacking and in-hand cube reorientation. These findings suggest that VQ-ACE offers a scalable and effective solution for robotic manipulation tasks involving complex, high-dimensional state spaces, contributing to more natural and adaptable robotic systems.

翻译：灵巧机器人操作因其在诸如手内操作和物体抓取等任务中所需手部运动的高维度和复杂性，仍然是一个重大挑战。本文通过引入向量量化动作分块嵌入（VQ-ACE）来解决这一问题，这是一个将人手运动压缩到量化潜在空间的新型框架，能在保留关键运动特征的同时显著降低动作空间的维度。通过将VQ-ACE与模型预测控制（MPC）和强化学习（RL）相结合，我们能够在使用仿生机器人手的灵巧操作任务中实现更高效的探索和策略学习。我们的结果表明，在诸如滚球和物体拾取等任务中，利用MPC进行潜在空间采样能产生更类人的行为，从而带来更高的任务成功率和更低的控制成本。对于RL，动作分块加速了学习过程并改善了探索效率，这在立方体堆叠和手内立方体重定向等任务中更快的收敛速度上得到了证明。这些发现表明，VQ-ACE为涉及复杂高维状态空间的机器人操作任务提供了一个可扩展且有效的解决方案，有助于构建更自然、适应性更强的机器人系统。