Robotic Manipulation Skill Acquisition Via Demonstration Policy Learning

Liu, Dong; Lu, Binpeng; Cong, Ming; Yu, Honghua; Zou, Qiang; Du, Yu

doi:10.1109/tcds.2021.3094269

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Motion-based tasks, like pick-and-place [36][37][38], focus on teaching the robot's movement patterns. The key information for success is captured in the robot's trajectory and kinematics, with limited and controlled contact with the environment [36][37][38][41][42][43][44][45][46][47][48]. Conversely, contact-based tasks, such as insertion [33,[49][50][51][52][53][54][55][56][57][58][59][60], require the robot to understand how to interact with objects.…”

Section: Motion-based Versus Contact-based Demonstrationmentioning

confidence: 99%

A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

Barekatain,

Habibi,

Voos

2024

Preprint

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This paper provides a structured and practical roadmap for practitioners to integrate Learning from Demonstration (LfD) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift from mass production to mass customization, it is crucial to have an easy-to-follow roadmap for practitioners with moderate expertise, to transform existing robotic processes to customizable LfD-based solutions. To realize this transformation, we devise the key questions of "What to Demonstrate", "How to Demonstrate", "How to Learn", and "How to Refine". To follow through these questions, our comprehensive guide offers a questionnaire-style approach, highlighting key steps from problem definition to solution refinement. The paper equips both researchers and industry professionals with actionable insights to deploy LfD-based solutions effectively. By tailoring the refinement criteria to manufacturing settings, the paper addresses related challenges and strategies for enhancing LfD performance in manufacturing contexts.

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Section: Motion-based Versus Contact-based Demonstrationmentioning

confidence: 99%

A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

Barekatain,

Habibi,

Voos

2024

Preprint

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“…Learning multi-step tasks such as stacking [16,17,18], assembling [19,20,21,22,23] and sorting [5,24] are always more challenging because more primitive actions and action constraints are involved. In ref.…”

Section: Related Workmentioning

confidence: 99%

Learn multi-step object sorting tasks through deep reinforcement learning

et al. 2022

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Robotic systems are usually controlled to repetitively perform specific actions for manufacturing tasks. The traditional control methods are domain-dependent and model-dependent with cost of much human efforts. They cannot meet the new requirements of generality and flexibility in many areas such as intelligent manufacturing and customized production. This paper develops a general model-free approach to enable robots to perform multi-step object sorting tasks through deep reinforcement learning. Taking projected heightmap images from different time steps as input without extra high-level image analysis and understanding, critic models are designed to produce a pixel-wise Q value map for each type of action. It is a new trial to apply pixel-wise Q value-based critic networks to solve multi-step sorting tasks that involve many types of actions and complex action constraints. The experimental validations on simulated and realistic object sorting tasks demonstrate the effectiveness of the proposed approach. Qualitative results (videos), code for simulated and realistic experiments, and pre-trained models are available at https://github.com/JiatongBao/DRLSorting

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