Sequence-based multimodal apprenticeship learning for robot perception and decision making

Apprenticeship learning has recently attracted a wide attention due to its capability of allowing robots to learn physical tasks directly from demonstrations provided by human experts. Most previous techniques assumed that the state space is known a priori or employed simple state representations that usually suffer from perceptual aliasing. Different from previous research, we propose a novel approach named Sequence-based Multimodal Apprenticeship Learning (SMAL), which is capable to simultaneously fusing temporal information and multimodal data, and to integrate robot perception with decision making. To evaluate the SMAL approach, experiments are performed using both simulations and real-world robots in the challenging search and rescue scenarios. The empirical study has validated that our SMAL approach can effectively learn plans for robots to make decisions using sequence of multimodal observations. Experimental results have also showed that SMAL outperforms the baseline methods using individual images.

Authors

• Fei Han 0002
• Xue Yang
• Yu Zhang
• Hao Zhang 0011

Keywords

• Decision making
• Feature extraction
• Robot sensing systems
• Cameras
• Learning (artificial intelligence)
• Multimodal Learning
• Learning Decision
• Perception Of The Robot
• Apprenticeship Learning
• State Space
• Individual Images
• Baseline Methods
• State Representation
• Human Experts
• Sequence Of Observations
• Transition State
• Local Features
• Weight Matrix
• Qualitative Results
• Representative Sequences
• Global Status
• Individual Modules
• Reward Function
• Markov Decision Process
• Template Sequence
• State Recognition
• Expert Demonstrations
• Markov Decision Process Model
• Inverse Reinforcement Learning
• Modal Features
• Real Robot
• Search Task
• Sparse Optimization
• Disaster Scenarios
• Obstacle Avoidance