Task Decoupling in Preference-based Reinforcement Learning for Personalized Human-Robot Interaction

Intelligent robots designed to interact with hu-mans in the real world need to adapt to the preferences of different individuals. Preference-based reinforcement learning (RL) has shown great potential for teaching robots to learn personalized behaviors from interacting with humans with-out a meticulous, hand-crafted reward function, replaced by learning reward based on a human's preferences between two robot trajectories. However, poor feedback efficiency and poor exploration in the state and reward spaces make current preference-based RL algorithms perform poorly in complex interactive tasks. To improve the performance of preference-based RL, we incorporate prior knowledge of the task into preference-based RL. Specifically, we decouple the task from preference in human-robot interaction. We utilize a sketchy task reward derived from task priori to instruct robots to conduct more effective task exploration. Then a learned reward from preference-based RL is used to optimize the robot's policy to align with human preferences. In addition, these two parts are combined organically via reward shaping. The experimental results show that our method is a practical and effective solution for personalized human-robot interaction. Code is available at https://github.com/Wenminggong/PbRL_for_PHRI.

Authors

• Mingjiang Liu
• Chunlin Chen

Keywords

• Codes
• Education
• Human-robot interaction
• Reinforcement learning
• Trajectory
• Behavioral sciences
• Task analysis
• Reward Function
• Reinforcement Learning Algorithm
• Interaction Task
• Human Preferences
• Reward Learning
• Robot Trajectory
• Reward Task
• Deep Learning
• High Velocity
• Stochastic Model
• Parametrized
• Optimal Policy
• Sampling Efficiency
• Deep Reinforcement Learning
• End-effector
• Task Goal
• Reinforcement Learning Methods
• Physical Simulation
• Sum Of Rewards
• Human Oral Cavity
• Reward Rate
• Algorithm In This Paper
• Predictor Of Preference