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Imitation learning allows robots to efficiently acquire complex tasks that are challenging to program manually. By learning from demonstrations, robots can adapt to new environments and task variations while enabling intuitive human-robot interaction. However, hardware constraints–such as motion limitations and sensory precision–often reduce its effectiveness. To mitigate these issues, we evaluate the Action Chunking with Transformers (ACT) algorithm, which reduces compounding errors and enhances learning efficiency. Through systematic experiments across two distinct task objectives under varying conditions, we assess ACT’s real-world applicability and its potential to accelerate robotic deployment.

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Real-World Feasibility Analysis of ACT Algorithm for Robotic Manipulation on Home

  • 会议论文

  • 发表日期：

    2026年

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  • Junlin Huang,
  • Feihong Liu,
  • Siyu Wang,
  • Yang Yang,
  • Yuhang Li,
  • Qiuyue Che,
  • Qiushuang Liu,
  • Deping Li

摘要

Imitation learning allows robots to efficiently acquire complex tasks that are challenging to program manually. By learning from demonstrations, robots can adapt to new environments and task variations while enabling intuitive human-robot interaction. However, hardware constraints–such as motion limitations and sensory precision–often reduce its effectiveness. To mitigate these issues, we evaluate the Action Chunking with Transformers (ACT) algorithm, which reduces compounding errors and enhances learning efficiency. Through systematic experiments across two distinct task objectives under varying conditions, we assess ACT’s real-world applicability and its potential to accelerate robotic deployment.