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Robotics

LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning

Hao Chen, Jiaming Liu, Zhonghao Yan

Featured May 17, 2026

Original Paper

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Simply

Robots usually just react, but this paper teaches them to "think" first using latent reasoning before acting, and even lets them decide how much to "think" based on how tricky the task is, making them much better at complex jobs.

In depth
The paper introduces LaST-R1, a novel reinforcement learning framework that enhances robotic manipulation by integrating latent reasoning directly into the policy optimization. It proposes Latent-to-Action Policy Optimization (LAPO), which jointly optimizes both the robot's internal thought process (latent Chain-of-Thought) and its physical actions, allowing environmental rewards to shape both. An adaptive latent CoT mechanism further enables the robot to dynamically adjust its reasoning depth based on task complexity, balancing efficiency and cognitive capacity.

Key Takeaways

  • 1
    Latent reasoning is explicitly integrated into robotic policies, allowing models to capture fine-grained physical dynamics before acting.
  • 2
    Latent-to-Action Policy Optimization (LAPO) jointly optimizes internal reasoning and external actions using environmental rewards, leading to superior physical world modeling.
  • 3
    An adaptive latent Chain-of-Thought (CoT) mechanism dynamically adjusts reasoning length, improving efficiency and generalization across diverse tasks.

Conceptual Flow

HIGH LEVEL
1
Methodology: How LaST-R1 Learns to Think and Act

The robot sees and hears, then "thinks" about what to do using hidden thoughts, and finally acts, learning from its mistakes.

Robot Sees
Robot Hears
Think & Plan
Robot Actions
2
Results: Smarter Robots, Better Performance

By learning to think and act together, the robot performs much better and adapts to new situations more easily than older methods.

Old Robot Method
LaST-R1 Robot
Compare Performance
Faster Learning
Higher Success
Adapts Better