International Journal For Multidisciplinary Research

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Imitation Learning for Robotics: Progress, Challenges, and Applications in Manipulation and Teleoperation

Author(s) Ruchik Kashyapkumar Thaker
Country Canada
Abstract The evolution of robotics has shifted its applications from industrial environments to more intelligent service scenarios, requiring adaptability in complex and uncertain conditions. Traditional manual coding methods struggle in these dynamic environments, making imitation learning (IL) a valuable approach for robotic manipulation by leveraging expert demonstrations. In this paper, I provide a review of the state-of-the-art in IL for robotic manipulation, focusing on three key aspects: demonstration, representation, and learning algorithms. I outline IL’s development history, taxonomies, and key milestones while discussing the challenges associated with learning strategies, such as dependency on demonstration quality and task-specific limitations. I also highlight potential areas for future research, including learning from suboptimal demonstrations, incorporating voice instructions, and optimizing learning policies. Additionally, I review approaches like "Mimic," which enhances teleoperation by allowing users to record and reuse robot trajectories, and Interactive Imitation Learning techniques that use human feedback in state-space to improve agent behavior. This survey provides insights into the current challenges of IL in robotic manipulation and explores promising directions for further research.
Keywords Imitation Learning (IL), Robotic Manipulation, Demonstration, Learning Algorithms, Machine Learning, Robot Teleoperation, Interactive Imitation Learning, Learning from Demonstration (LfD)
Field Computer > Automation / Robotics
Published In Volume 5, Issue 3, May-June 2023
Published On 2023-06-08
Cite This Imitation Learning for Robotics: Progress, Challenges, and Applications in Manipulation and Teleoperation - Ruchik Kashyapkumar Thaker - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.29706
DOI https://doi.org/10.36948/ijfmr.2023.v05i03.29706
Short DOI https://doi.org/g8pnjb
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