Research Paper#Robotics, Computer Vision, Reinforcement Learning🔬 ResearchAnalyzed: Jan 3, 2026 17:09
Adaptive Working Memory for Robot Manipulation
Published:Dec 31, 2025 05:20
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of state ambiguity in robot manipulation, a common problem where identical observations can lead to multiple valid behaviors. The proposed solution, PAM (Policy with Adaptive working Memory), offers a novel approach to handle long history windows without the computational burden and overfitting issues of naive methods. The two-stage training and the use of hierarchical feature extraction, context routing, and a reconstruction objective are key innovations. The paper's focus on maintaining high inference speed (above 20Hz) is crucial for real-world robotic applications. The evaluation across seven tasks demonstrates the effectiveness of PAM in handling state ambiguity.
Key Takeaways
- •Addresses state ambiguity in robot manipulation.
- •Proposes PAM, a novel visuomotor policy with Adaptive working Memory.
- •Employs a two-stage training process.
- •Utilizes hierarchical feature extraction, context routing, and a reconstruction objective.
- •Achieves high inference speed (above 20Hz) with a 300-frame history window.
- •Demonstrates effectiveness across multiple tasks.
Reference
“PAM supports a 300-frame history window while maintaining high inference speed (above 20Hz).”