From Semantics to Execution: Integrating Action Planning With Reinforcement Learning for Robotic Causal...

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Reinforcement learning (RL) is effective for robot control but struggles with high-level planning.
• Symbolic action planning addresses causal dependencies but uses discrete spaces, conflicting with RL's continuous nature.
• Integrating these approaches is challenging due to differing state and action space representations.

Purpose of the Study:

• To integrate symbolic action planning with model-free reinforcement learning.
• To demonstrate how reward sparsity can bridge discrete and continuous state-action spaces.
• To enable robots to solve complex tasks with causal dependencies under noisy conditions.

Main Methods:

• Leveraging recent advances in model-free reinforcement learning, including universal value function approximators and hindsight experience replay.
• Utilizing sparse rewards as a bridge between high-level symbolic planning and low-level continuous control.
• Developing an integrated method that combines symbolic action planning with reinforcement learning.

Main Results:

• The integrated method successfully solves robotic tasks with non-trivial causal dependencies.
• The approach demonstrates robustness under noisy conditions.
• The system effectively exploits both data-driven learning and prior knowledge.

Conclusions:

• Integrating symbolic action planning and reinforcement learning is feasible and effective.
• Reward sparsity is a key mechanism for bridging different representational spaces in robotics.
• The proposed method offers a promising direction for complex robot control and task achievement.