Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning.

Jianlan Luo, Charles Xu, Jeffrey Wu, Sergey Levine

August 2025 Sci Robot

Synopsis of Social media discussions

The discussions highlight the groundbreaking nature of the research, often emphasizing its rapid training times and ability to perform complex, dexterous tasks with keywords like 'precise', 'real-world', and 'human feedback'. The tone remains excited and optimistic, reflecting a belief in its transformative potential.

A
Agreement
 Moderate agreement

Most discussions express strong support and enthusiasm for the research, highlighting its effectiveness and innovative approach.

I
Interest
 High level of interest

The discussions indicate high curiosity about the methodologies and potential applications of the system, with mentions of rapid task mastery and real-world impact.

E
Engagement
 High engagement

Many posts delve into specifics, referencing concepts like human-in-the-loop reinforcement learning, real-time training, and advancements over traditional methods, showing deep engagement.

I
Impact
 High level of impact

The posts suggest this research could significantly influence robotics, with phrases like 'game changer' and references to speed and precision improvements implying high potential impact.

Social Mentions

YouTube

3 Videos

Bluesky

1 Posts

Twitter

101 Posts

Blogs

2 Articles

News

9 Articles

Metrics

Video Views

58,691

Total Likes

869

Extended Reach

2,359,264

Social Features

116

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

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2,963 views

  • 大曽根宏幸 / Hiroyuki Osone
    @OsoneHiroyuki (Twitter)

    Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning | Science Robotics https://t.co/Q2ZjDPA0Q2
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    @Toukairinn_FUZZ (Twitter)

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  • arxiv cs.RO
    @arxiv-cs-ro.bsky.social (Bluesky)

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    October 30, 2024

Abstract Synopsis

  • Robotic manipulation is a key challenge in robotics, with current methods facing limitations in design, performance, and data needs, affecting their real-world application.
  • Reinforcement learning (RL) offers an alternative by allowing robots to learn complex skills through interaction, but challenges like sample efficiency and safety remain.
  • Our human-in-the-loop, vision-based RL system has achieved significant improvements in dexterous manipulation tasks in the real world, doubling task success rates and increasing execution speed within just a couple of hours of training.