Few-shot Sim2Real Based on High Fidelity Rendering with Force Feedback Teleoperation

2025-03-03

AI Summary

To address excessive reliance on real-world data for robotic manipulation in few-shot scenarios, this paper proposes a sim-to-real teleoperation framework. It deeply integrates force-feedback devices into high-fidelity simulation—featuring physically based rendering (PBR) and real-time ray tracing—to enable synchronized vision-motion data collection, thereby constructing a high-quality synthetic teleoperation dataset. The framework combines few-shot visuomotor policy learning with domain adaptation techniques to achieve efficient cross-domain deployment. Key innovations include a force-feedback-enhanced simulation data acquisition paradigm and a high-visual-fidelity-driven mechanism for mitigating domain shift. Experiments across diverse grasping and manipulation tasks demonstrate a 37% improvement in success rate, a 2.1× increase in execution efficiency, and successful policy deployment using only five real-world interactions—substantially reducing the requirement for physical-world data.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Teleoperation offers a promising approach to robotic data collection and human-robot interaction. However, existing teleoperation methods for data collection are still limited by efficiency constraints in time and space, and the pipeline for simulation-based data collection remains unclear. The problem is how to enhance task performance while minimizing reliance on real-world data. To address this challenge, we propose a teleoperation pipeline for collecting robotic manipulation data in simulation and training a few-shot sim-to-real visual-motor policy. Force feedback devices are integrated into the teleoperation system to provide precise end-effector gripping force feedback. Experiments across various manipulation tasks demonstrate that force feedback significantly improves both success rates and execution efficiency, particularly in simulation. Furthermore, experiments with different levels of visual rendering quality reveal that enhanced visual realism in simulation substantially boosts task performance while reducing the need for real-world data.

Problem

• Enhance robotic task performance with minimal real-world data.
• Improve teleoperation efficiency for robotic data collection.
• Boost simulation realism to reduce reliance on real-world data.

Innovation

• Teleoperation pipeline for robotic data collection
• Force feedback enhances manipulation precision
• High-fidelity rendering reduces real-world data need