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-$\alpha$). 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-$\alpha$ 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-$\alpha$. 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-$\alpha$ can be seen through comprehensive real-world experiments. Video available at: https://mertcookimg.github.io/alpha-biact/

翻译：日常任务中的自主操作需要灵活的动作生成，以应对复杂多样的现实环境，例如处理不同软硬度的物体。模仿学习（IL）使机器人能够从专家演示中学习复杂任务。然而，许多现有方法依赖于位置/单向控制，在需要力信息/控制的任务中（如小心抓取易碎或硬度变化的物体）仍面临挑战。随着对多样化控制需求的增长，对考虑多种运动输入的低成本双臂机器人存在需求。为应对这些挑战，我们提出了基于双边控制的模仿学习与Transformer动作分块方法（Bi-ACT），以及面向日常双臂机器人操作研究的低成本物理硬件平台（ALPHA-α）。Bi-ACT利用双边控制同时采用位置和力信息，增强了机器人对物体特性（如硬度、形状和重量）的适应性。ALPHA-α的设计理念在于可负担性、易用性、可维修性、易于组装以及多样化的控制模式（位置、速度、扭矩），允许研究人员/开发者自由使用ALPHA-α构建控制系统。在我们的实验中，我们对Bi-ACT在单臂操作任务中进行了详细分析，确认了其相较于无力控制的Bi-ACT具有更优的性能和适应性。基于这些结果，我们将Bi-ACT应用于双臂操作任务。实验结果表明，在多个任务中，协调的双臂操作均实现了高成功率。通过全面的真实世界实验，验证了Bi-ACT与ALPHA-α的有效性。视频可见于：https://mertcookimg.github.io/alpha-biact/