Likelihood confidence rating based multi-modal information fusion for robot fine operation

Multi-modal information fusion plays an important role in many robotic applications, such as target grasping, manipulation and fine operation. Traditional fusion strategies, e.g. Bayesian fusion, directly adopt each uni-modal likelihood without giving enough attention to the fact that all these likelihoods are often vulnerable to sample data and modality-specific identification algorithm, which could possibly incur inaccuracy of, say, target recognition in a practical application. To address this issue, the paper presents a likelihood confidence rating strategy to fix traditional Bayesian fusion. Due to the great importance to the modalities with more accurate likelihoods, the strategy is capable of assigning different weights to each modality meticulously. We extensively evaluate the proposed strategy on our dextrous robotic hand testbed. The results demonstrate that the proposed method can achieve significant improvement in terms of fused classification performance.