The rapid development of collaborative robotics has provided a new
possibility of helping the elderly who has difficulties in daily life, allowing
robots to operate according to specific intentions. However, efficient
human-robot cooperation requires natural, accurate and reliable intention
recognition in shared environments. The current paramount challenge for this is
reducing the uncertainty of multimodal fused intention to be recognized and
reasoning adaptively a more reliable result despite current interactive
condition. In this work we propose a novel learning-based multimodal fusion
framework Batch Multimodal Confidence Learning for Opinion Pool (BMCLOP). Our
approach combines Bayesian multimodal fusion method and batch confidence
learning algorithm to improve accuracy, uncertainty reduction and success rate
given the interactive condition. In particular, the generic and practical
multimodal intention recognition framework can be easily extended further. Our
desired assistive scenarios consider three modalities gestures, speech and
gaze, all of which produce categorical distributions over all the finite
intentions. The proposed method is validated with a six-DoF robot through
extensive experiments and exhibits high performance compared to baselines.

该论文提出了一种新颖的基于学习的多模态融合框架 (BMCLOP) 用于辅助老年人，该框架结合贝叶斯多模态融合方法和批量置信度学习算法，提高准确性、降低不确定性并在交互条件下获得更可靠的结果。