用位置力双控提升机器人抓取柔韧物体的适应性。
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
- 引入双控模仿学习框架，同时利用位置与力反馈信息。
- 在多任务中实现高成功率的单/双手协同操作。
- 低成本硬件系统支持多种控制模式，适合科研开发。
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变的真实环境，如不同硬度和软度的物体。模仿学习（IL）使机器人能够从专家示范中学习复杂任务。然而，现有方法大多依赖位置或单边控制，难以处理需力信息的任务，例如轻柔抓取易碎或硬度各异的物体。随着多样化控制需求增加，亟需支持多种运动输入的低成本双臂机器人。为此，我们提出基于动作分块与变换器的双控模仿学习（Bi-ACT）以及适用于日常双臂机器人研究的低成本物理硬件系统ALPHA-α。Bi-ACT通过双控融合位置与力信息，增强机器人对物体硬度、形状和重量等特性的适应能力。ALPHA-α强调成本低、易用、可维修、易组装及支持位置、速度、扭矩等多种控制模式，便于研究人员自由构建控制系统。实验表明，相较于无力控的Bi-ACT，其在单臂任务中表现更优；进一步应用于双臂任务时，在多个操作中均取得高成功率。真实世界实验充分验证了Bi-ACT与ALPHA-α的有效性。
原文摘要 · Abstract (English)
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/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。