VQ-ACE：通过动作分块嵌入实现灵巧机器人操作的高效策略搜索
VQ-ACE: Efficient Policy Search for Dexterous Robotic Manipulation via Action Chunking Embedding
AI总结：
VQ-ACE通过压缩人类手部运动到量化潜在空间，结合MPC和RL，在灵巧操作任务中实现高效策略搜索，提升成功率并加速学习。
摘要：
灵巧机器人操作仍然是一个重大挑战，因为手部运动的高维度和复杂性，例如手中操作和物体抓取等任务所需。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，这是一种新颖的框架，将人类手部运动压缩到量化潜在空间中，显著降低动作空间的维度，同时保留关键运动特征。通过将VQ-ACE与模型预测控制（MPC）和强化学习（RL）相结合，我们能够在仿生机器人手上实现更高效的探索和策略学习，用于灵巧操作任务。我们的结果表明，使用MPC进行潜在空间采样在诸如滚球和物体拾取等任务中产生更类人的行为，导致更高的任务成功率和更低的控制成本。对于RL，动作分块加速学习并改善探索，通过在堆叠立方体和手中立方体重定向等任务中更快的收敛来证明。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种可扩展且有效的解决方案，有助于实现更自然和适应性更强的机器人系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
