Towards cost-effective and safe contact-rich robotic manipulation with reinforcement learning: A review of techniques for future industrial automation

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Improvements in cost and safety of reinforcement learning for contact-rich manipulation tasks indicate significant progress.
Recent techniques offer enhanced scalability and knowledge transfer for reinforcement learning applications in robotics.
The analysis emphasizes further research into safety concerns and inter-robot policy transfer for more effective automation.
Advancements in contact-rich robotic manipulation can enable broader adoption and versatility in industrial automation.

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Review highlights improvements in safety and cost of reinforcement learning for contact-rich tasks, suggesting future research directions.

Parnada et al. (2025) studied this question.

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Review highlights improvements in safety and cost of reinforcement learning for contact-rich tasks, suggesting future research directions.