ALPHA-$α$ 与 Bi-ACT 足矣：面向低成本系统下单臂与双臂机器人操作模仿学习的位置与力信息/控制的重要性
ALPHA-$α$ and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System
AI总结：
该研究提出支持多控制模式的低成本双臂硬件平台ALPHA-$α$，以及融合位置与力信息的双侧控制模仿学习方法Bi-ACT，经单双臂操作实验验证，其性能与适应性更优，可高效完成复杂真实操作任务。
摘要：
日常任务中的自主操作需要灵活的动作生成能力，以应对复杂多样的真实世界环境，例如硬度各不相同的物体。模仿学习（Imitation Learning，IL）使机器人能够从专家演示中学习复杂任务。然而，现有许多方法依赖位置/单侧控制，在需要力信息/控制的任务中仍存在挑战，例如小心抓取易碎物体或硬度各异的物体。随着对多样化控制的需求不断增长，业界需要考虑多种电机输入的低成本双臂机器人。为应对这些挑战，我们提出了基于双侧控制的动作分块Transformer模仿学习方法（Bilateral Control-Based Imitation Learning via Action Chunking with Transformers，Bi-ACT），以及面向日常双臂机器人操作研究的、考虑多种电机控制模式的低成本物理硬件平台（"A" "L"ow-cost "P"hysical "Ha"rdware Considering Diverse Motor Control Modes for Research in Everyday Bimanual Robotic Manipulation，ALPHA-$α$）。Bi-ACT利用双侧控制同时运用位置与力信息，增强机器人对物体硬度、形状、重量等特性的适应能力。ALPHA-$α$的设计理念是可负担、易用、可维修、易组装，且支持多种控制模式（位置、速度、扭矩），让研究人员与开发者能够借助ALPHA-$α$自由搭建控制系统。在实验中，我们针对单臂操作任务对Bi-ACT开展了详细分析，证实其性能与适应性均优于无力控版本的Bi-ACT。基于这些结果，我们将Bi-ACT应用于双臂操作任务，实验结果显示其在多项任务的双臂协同操作中均取得了高成功率。通过全面的真实世界实验，Bi-ACT与ALPHA-$α$的有效性得到了验证。相关视频可访问：https://mertcookimg.github.io/alpha-biact/
英文摘要：
Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness. Imitation Learning (IL) enables robots to learn complex tasks from expert demonstrations. However, a lot of existing methods rely on position/unilateral control, leaving challenges in tasks that require force information/control, like carefully grasping fragile or varying-hardness objects. As the need for diverse controls increases, there are demand for low-cost bimanual robots that consider various motor inputs. To address these challenges, we introduce Bilateral Control-Based Imitation Learning via Action Chunking with Transformers(Bi-ACT) and"A" "L"ow-cost "P"hysical "Ha"rdware Considering Diverse Motor Control Modes for Research in Everyday Bimanual Robotic Manipulation (ALPHA-$α$). Bi-ACT leverages bilateral control to utilize both position and force information, enhancing the robot's adaptability to object characteristics such as hardness, shape, and weight. The concept of ALPHA-$α$ is affordability, ease of use, repairability, ease of assembly, and diverse control modes (position, velocity, torque), allowing researchers/developers to freely build control systems using ALPHA-$α$. In our experiments, we conducted a detailed analysis of Bi-ACT in unimanual manipulation tasks, confirming its superior performance and adaptability compared to Bi-ACT without force control. Based on these results, we applied Bi-ACT to bimanual manipulation tasks. Experimental results demonstrated high success rates in coordinated bimanual operations across multiple tasks. The effectiveness of the Bi-ACT and ALPHA-$α$ can be seen through comprehensive real-world experiments. Video available at: https://mertcookimg.github.io/alpha-biact/
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。
