Data Scaling Laws in Imitation Learning for Robotic Manipulation
Data scaling has revolutionized fields like natural language processing and
computer vision, providing models with remarkable generalization capabilities.
In this paper, we investigate whether similar data scaling laws exist in
robotics, particularly in robotic manipulation, and whether appropriate data
scaling can yield single-task robot policies that can be deployed zero-shot for
any object within the same category in any environment. To this end, we conduct
a comprehensive empirical study on data scaling in imitation learning. By
collecting data across numerous environments and objects, we study how a
policy's generalization performance changes with the number of training
environments, objects, and demonstrations. Throughout our research, we collect
over 40,000 demonstrations and execute more than 15,000 real-world robot
rollouts under a rigorous evaluation protocol. Our findings reveal several
intriguing results: the generalization performance of the policy follows a
roughly power-law relationship with the number of environments and objects. The
diversity of environments and objects is far more important than the absolute
number of demonstrations; once the number of demonstrations per environment or
object reaches a certain threshold, additional demonstrations have minimal
effect. Based on these insights, we propose an efficient data collection
strategy. With four data collectors working for one afternoon, we collect
sufficient data to enable the policies for two tasks to achieve approximately
90% success rates in novel environments with unseen objects.
671ba8a2663623341c70f396
Fanqi Lin
671ba8a2663623341c70f397
Yingdong Hu
671ba8a2663623341c70f398
Pingyue Sheng
671ba8a2663623341c70f399
Chuan Wen
671ba8a2663623341c70f39a
Jiacheng You
671ba8a2663623341c70f39b
Yang Gao
Data Scaling Laws in Imitation Learning for Robotic Manipulation
Data scaling has revolutionized fields like natural language processing and
computer vision, providing models with remarkable generalization capabilities.
In this paper, we investigate whether similar data scaling laws exist in
robotics, particularly in robotic manipulation, and whether appropriate data
scaling can yield single-task robot policies that can be deployed zero-shot for
any object within the same category in any environment. To this end, we conduct
a comprehensive empirical study on data scaling in imitation learning. By
collecting data across numerous environments and objects, we study how a
policy's generalization performance changes with the number of training
environments, objects, and demonstrations. Throughout our research, we collect
over 40,000 demonstrations and execute more than 15,000 real-world robot
rollouts under a rigorous evaluation protocol. Our findings reveal several
intriguing results: the generalization performance of the policy follows a
roughly power-law relationship with the number of environments and objects. The
diversity of environments and objects is far more important than the absolute
number of demonstrations; once the number of demonstrations per environment or
object reaches a certain threshold, additional demonstrations have minimal
effect. Based on these insights, we propose an efficient data collection
strategy. With four data collectors working for one afternoon, we collect
sufficient data to enable the policies for two tasks to achieve approximately
90% success rates in novel environments with unseen objects.
Research on data scaling in robotic manipulation reveals that generalization performance improves with environmental and object diversity rather than the number of demonstrations, leading to efficient data collection strategies for zero-shot deployment.
data scaling imitation learning generalization performance power-law relationship data collection strategy zero-shot deployment