CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation
We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment, CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world, handling diverse physical and visual conditions. Regardless of its affordability and convenience in data collection, CyberDemo outperforms baseline methods in terms of success rates across various tasks and exhibits generalizability with previously unseen objects. For example, it can rotate novel tetra-valve and penta-valve, despite human demonstrations only involving tri-valves. Our research demonstrates the significant potential of simulated human demonstrations for real-world dexterous manipulation tasks. More details can be found at https://cyber-demo.github.io
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationImitation LearningSimilar Papers 제목 키워드 기반
Cross-Domain Transfer via Semantic Skill Imitation
We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL) in a different target domain, e.g. a robotic manipulator in a si…
Reinforcement Learning (RL)Robot ManipulationNeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis
Expert demonstrations are a rich source of supervision for training visual robotic manipulation policies, but imitation learning methods often require either a large number of demonstrations or expensive online expert su…
Data AugmentationImitation LearningNeRFNovel View Synthesis+1Closed-loop Teaching via Demonstrations to Improve Policy Transparency
Demonstrations are a powerful way of increasing the transparency of AI policies. Though informative demonstrations may be selected a priori through the machine teaching paradigm, student learning may deviate from the pre…
Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning
The use of human demonstrations in reinforcement learning has proven to significantly improve agent performance. However, any requirement for a human to manually 'teach' the model is somewhat antithetical to the goals of…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Calibrating Generative AI to Produce Realistic Essays for Data Augmentation
Data augmentation can mitigate limited training data in machine-learning automated scoring engines for constructed response items. This study seeks to determine how well three approaches to large language model prompting…
Data Augmentation