title: Data Scaling Laws in Imitation Learning for Robotic Manipulation
summary: 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.
paper_authors._id: 671ba8a2663623341c70f396
paper_authors.name: Fanqi Lin
paper_authors._id: 671ba8a2663623341c70f397
paper_authors.name: Yingdong Hu
paper_authors._id: 671ba8a2663623341c70f398
paper_authors.name: Pingyue Sheng
paper_authors._id: 671ba8a2663623341c70f399
paper_authors.name: Chuan Wen
paper_authors._id: 671ba8a2663623341c70f39a
paper_authors.name: Jiacheng You
paper_authors._id: 671ba8a2663623341c70f39b
paper_authors.name: Yang Gao
paper_title: Data Scaling Laws in Imitation Learning for Robotic Manipulation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: data scaling imitation learning generalization performance power-law relationship data collection strategy zero-shot deployment