Sequence-based Multimodal Apprenticeship Learning For Robot Perception and Decision Making
This addresses the challenge of perceptual aliasing in apprenticeship learning for robots, though it appears incremental as it builds on existing techniques with multimodal fusion.
The paper tackles the problem of robots learning tasks from demonstrations by proposing SMAL, which fuses temporal and multimodal data to integrate perception and decision-making, showing it outperforms baseline methods using individual images in search and rescue scenarios.
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.