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Related Experiment Videos

Motion Planning-Augmented Hierarchical Reinforcement Learning for Long-Horizon Mobile Manipulation.

Hyungtai Kim1, Mun-Taek Choi1

  • 1School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

We developed a novel hierarchical reinforcement learning method for long-horizon mobile manipulation tasks. This approach enhances sample efficiency and ensures reliable task hand-offs, improving robot performance in complex indoor environments.

Keywords:
mobile robotsmotion planningreinforcement learningrobot kinematicsservice robots

Related Experiment Videos

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Long-horizon mobile manipulation involves sequential subtasks like navigation and object manipulation.
  • Standard reinforcement learning faces challenges with reward sparsity and exploration.
  • Hierarchical methods struggle with reliable hand-offs between subtasks due to kinematic infeasibility.

Purpose of the Study:

  • To propose a motion planning-augmented hierarchical reinforcement learning architecture.
  • To address sample efficiency and hand-off reliability in long-horizon mobile manipulation.
  • To improve robot performance in complex indoor environments.

Main Methods:

  • Decomposed tasks into subtasks using a Semi-Markov Decision Process.
  • Embedded collision-free trajectories (RRT*) as reward shaping signals.
  • Utilized a region-goal mechanism based on inverse kinematics for continuous hand-offs.

Main Results:

  • The proposed method significantly improved subtask success rates.
  • Enhanced sample efficiency compared to baseline methods.
  • Performance gains compounded across the long-horizon task chain.

Conclusions:

  • The motion planning-augmented hierarchical reinforcement learning architecture is effective for long-horizon mobile manipulation.
  • The approach successfully resolves trade-offs between sample efficiency and hand-off reliability.
  • This method offers a robust solution for complex robotic tasks in indoor settings.