Dual Arm
Dual-arm robotics focuses on developing robots capable of performing complex manipulation tasks using two arms, mimicking human dexterity and efficiency. Current research emphasizes improving grasping capabilities through reinforcement learning, generative models (like diffusion models), and advanced planning algorithms (including those leveraging large language models and directed acyclic graphs) to coordinate complex, multi-step actions. This field is crucial for advancing automation in various sectors, from manufacturing and logistics to healthcare and assistive technologies, by enabling robots to handle a wider range of tasks more effectively and safely than single-arm systems.
Papers
DA-VIL: Adaptive Dual-Arm Manipulation with Reinforcement Learning and Variable Impedance Control
Md Faizal Karim, Shreya Bollimuntha, Mohammed Saad Hashmi, Autrio Das, Gaurav Singh, Srinath Sridhar, Arun Kumar Singh, Nagamanikandan Govindan, K Madhava Krishna
Image-Based Visual Servoing for Enhanced Cooperation of Dual-Arm Manipulation
Zizhe Zhang, Yuan Yang, Wenqiang Zuo, Guangming Song, Aiguo Song, Yang Shi