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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Hierarchical planning with state abstractions for temporal task specifications.

Yoonseon Oh1, Roma Patel2, Thao Nguyen2

  • 1Department of Electronic Engineering, Hanyang University, Seoul, 04763 Republic of Korea.

Autonomous Robots
|June 13, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new method, Abstract Product Markov Decision Process (AP-MDP), to help robots understand complex commands involving both time and abstraction. This approach significantly speeds up task planning for robots in various environments.

Keywords:
Hierarchical task planningLanguageLinear temporal logic

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Robots often receive task instructions using natural language with temporal and abstract elements.
  • Existing methods separately handle temporal logic (syntactically co-safe Linear Temporal Logic) and hierarchical abstractions (Abstract Markov Decision Processes).
  • A unified approach is needed to interpret commands involving both temporal ordering and spatial abstraction.

Purpose of the Study:

  • Introduce the Abstract Product Markov Decision Process (AP-MDP) framework.
  • Enable robots to interpret and execute complex language commands with multiple levels of abstraction and temporal ordering.
  • Improve the efficiency of robot task planning.

Main Methods:

  • Developed the AP-MDP framework to represent non-Markovian reward functions across different abstraction levels.
  • Translated Linear Temporal Logic (LTL) specifications into corresponding automata.
  • Created a product Markov Decision Process (MDP) integrating LTL specifications with environment MDPs.
  • Decomposed planning problems into manageable subproblems for efficient computation.
  • Trained a neural sequence-to-sequence model to convert language commands into LTL expressions.
  • Compiled a new corpus of non-Markovian language commands.

Main Results:

  • AP-MDP demonstrated faster performance compared to non-hierarchical methods in over 90% of path planning tasks.
  • Performance gains increased with larger environment domains.
  • In a cleanup world domain, AP-MDP achieved faster task completion in over 80% of scenarios.
  • The framework successfully enabled drones to execute abstract temporal commands in diverse indoor and outdoor settings.

Conclusions:

  • The AP-MDP framework effectively integrates temporal logic and hierarchical abstractions for robot task planning.
  • This novel approach significantly enhances the efficiency of robot command interpretation and execution.
  • The developed system shows promise for real-world applications, enabling robots to handle complex, natural language instructions.