MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents. However, the demonstrations can be extremely costly and time-consuming to collect. We introduce MimicGen, a system for automatically synthesizing large-scale, rich datasets from only a small number of human demonstrations by adapting them to new contexts. We use MimicGen to generate over 50K demonstrations across 18 tasks with diverse scene configurations, object instances, and robot arms from just ~200 human demonstrations. We show that robot agents can be effectively trained on this generated dataset by imitation learning to achieve strong performance in long-horizon and high-precision tasks, such as multi-part assembly and coffee preparation, across broad initial state distributions. We further demonstrate that the effectiveness and utility of MimicGen data compare favorably to collecting additional human demonstrations, making it a powerful and economical approach towards scaling up robot learning. Datasets, simulation environments, videos, and more at https://mimicgen.github.io .
Code (2)
Tasks
Imitation LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SoftMimicGen: A Data Generation System for Scalable Robot Learning in Deformable Object Manipulation
Large-scale robot datasets have facilitated the learning of a wide range of robot manipulation skills, but these datasets remain difficult to collect and scale further, owing to the intractable amount of human time, effo…
Synthetic Data GenerationRobot ManipulationDexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
Imitation learning from human demonstrations is an effective means to teach robots manipulation skills. But data acquisition is a major bottleneck in applying this paradigm more broadly, due to the amount of cost and hum…
Imitation LearningDynaMimicGen: A Data Generation Framework for Robot Learning of Dynamic Tasks
Learning robust manipulation policies typically requires large and diverse datasets, the collection of which is time-consuming, labor-intensive, and often impractical for dynamic environments. In this work, we introduce …
HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning
Imitation learning is a promising approach for training humanoid robots to both walk and manipulate, but it requires a large number of demonstrations, which are time-intensive and difficult to collect via teleoperation. …
SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment
Imitation learning from human demonstrations is an effective paradigm for robot manipulation, but acquiring large datasets is costly and resource-intensive, especially for long-horizon tasks. To address this issue, we pr…
Imitation LearningMotion PlanningRobot Manipulation