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Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

111
Citations
May 5, 2025
Published Date

Research Abstract & Technology Focus

Reinforcement learning (RL), particularly its combination with deep neural networks, referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophisticated robotic behaviors. Robotics problems, however, pose fundamental difficulties for the application of RL, stemming from the complexity and cost of interacting with the physical world. This article provides a modern survey of DRL for robotics, with a particular focus on evaluating the real-world successes achieved with DRL in realizing several key robotic competencies. Our analysis aims to identify the key factors underlying those exciting successes, reveal underexplored areas, and provide an overall characterization of the status of DRL in robotics. We highlight several important avenues for future work, emphasizing the need for stable and sample-efficient real-world RL paradigms; holistic approaches for discovering and integrating various competencies to tackle complex long-horizon, open-world tasks; and principled development and evaluation procedures. This survey is designed to offer insights for both RL practitioners and roboticists toward harnessing RL's power to create generally capable real-world robotic systems.
reinforcement learning robotics survey real-world successes
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Correlated Market Trend: Adaptive Learning

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Frequently Asked Questions (FAQ)

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What is the core focus of the research titled 'Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes'?

This literature focuses on: Reinforcement learning (RL), particularly its combination with deep neural networks, referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophistic...

Are there open-source GitHub repositories related to Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes?

Yes, open-source projects like THU-MAIC/OpenMAIC (Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click) are actively building upon these concepts.

Which startups are commercializing the technology behind Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes?

Products like Padel Chess are bringing this to market. Their focus is: Padel tactics learning app.

What other academic literature is closely related to 'Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes'?

Yes, highly correlated activity was mapped. An entry titled 'DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning' discusses this: Abstract General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exe...

Are there commercial applications of 'Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes' in market news publications?

Yes, highly correlated activity was mapped. An entry titled 'Getting robots back on track by reconstituting control in unexpected situations with online learning' discusses this: Robots struggle to operate when unexpected disturbances arise. The authors introduce a fast learning method that restores control in real time, red...

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