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Sim2Real (Simulation to Reality) techniques have gained prominence in roboticmanipulation and motion planning due to their ability to enhance success ratesby enabling agents to test and evaluate various policies and trajectories. Inthis paper, we investigate the advantages of integrating Sim2Real into roboticframeworks. We introduce the Triple Regression Sim2Real framework, whichconstructs a real-time digital twin. This twin serves as a replica of realityto simulate and evaluate multiple plans before their execution in real-worldscenarios. Our triple regression approach addresses the reality gap by: (1)mitigating projection errors between real and simulated camera perspectivesthrough the first two regression models, and (2) detecting discrepancies inrobot control using the third regression model. Experiments on 6-DoF grasp andmanipulation tasks (where the gripper can approach from any direction)highlight the effectiveness of our framework. Remarkably, with only RGB inputimages, our method achieves state-of-the-art success rates. This researchadvances efficient robot training methods and sets the stage for rapidadvancements in robotics and automation.

Triple Regression for Camera Agnostic Sim2Real Robot Grasping and Manipulation Tasks