Triple Regression for Camera Agnostic Sim2Real Robot Grasping and Manipulation Tasks

TL;DR

This paper presents the Triple Regression Sim2Real framework that creates a real-time digital twin to improve robotic grasping and manipulation success rates using only RGB images, effectively bridging reality gaps.

Contribution

The introduction of a triple regression approach that constructs a digital twin for simulating and evaluating robotic plans before real-world execution, addressing projection and control discrepancies.

Findings

Achieves state-of-the-art success rates with RGB input images.

Effectively mitigates projection errors and control discrepancies.

Enhances robotic training efficiency and accuracy.

Abstract

Sim2Real (Simulation to Reality) techniques have gained prominence in robotic manipulation and motion planning due to their ability to enhance success rates by enabling agents to test and evaluate various policies and trajectories. In this paper, we investigate the advantages of integrating Sim2Real into robotic frameworks. We introduce the Triple Regression Sim2Real framework, which constructs a real-time digital twin. This twin serves as a replica of reality to simulate and evaluate multiple plans before their execution in real-world scenarios. Our triple regression approach addresses the reality gap by: (1) mitigating projection errors between real and simulated camera perspectives through the first two regression models, and (2) detecting discrepancies in robot control using the third regression model. Experiments on 6-DoF grasp and manipulation tasks (where the gripper can approach from any direction) highlight the effectiveness of our framework. Remarkably, with only RGB input images, our method achieves state-of-the-art success rates. This research advances efficient robot training methods and sets the stage for rapid advancements in robotics and automation.