Adversarial generative learning and timed path optimization for real-time visual image prediction to guide robot arm movements

Abstract

Real-time visual image prediction, crucial for directing robotic arm movements, represents a significant technique in artificial intelligence and robotics. The primary technical challenges involve the robot’s inaccurate perception and understanding of the environment, coupled with imprecise control of movements. This study proposes ForGAN-MCTS, a generative adversarial network-based action sequence prediction algorithm, aimed at refining visually guided rearrangement planning for movable objects. Initially, the algorithm unveils a scalable and robust strategy for rearrangement planning, capitalizing on the capabilities of a Monte Carlo Tree Search strategy. Secondly, to enable the robot’s successful execution of grasping maneuvers, the algorithm proposes a generative adversarial network-based real-time prediction method, employing a network trained solely on synthetic data for robust estimation of multi-object workspace states via a single uncalibrated RGB camera. The efficacy of the newly proposed algorithm is corroborated through extensive experiments conducted by using a UR-5 robotic arm. The experimental results demonstrate that the algorithm surpasses existing methods in terms of planning efficacy and processing speed. Additionally, the algorithm is robust to camera motion and can effectively mitigate the effects of external perturbations.