Sequential Prediction with Logic Constraints for Surgical Robotic Activity Recognition

Md. Masudur Rahman, Richard M. Voyles, Juan P. Wachs, Yexiang Xue

Abstract: Many real-world time-sensitive and high-stake applications (e.g., surgical, rescue, and recovery robotics) exhibit sequential nature; thus, applying Recurrent Neural Network (RNN)-based sequential models is an attractive approach to detect robotic activity. One limitation of such approaches is data scarcity. As a result, limited training samples may lead to over-fitting, producing incorrect predictions during deployment. Nevertheless, abundant domain knowledge may still be available, which may help formulate logic constraints. In this paper, we propose a novel way to integrate domain knowledge into RNN-based sequential prediction. We build a Markov Logic Network (MLN)-based classifier that automatically learns constraint weights from data. We propose two methods to incorporate this MLN-based prediction: (i) PriorLayer, in which the values of the hidden layer of the RNN are combined with weights learned from logic constraints in an additional neural network layer, and (ii) Conflation, in which class probabilities from RNN predictions and constraint weights are combined based on the conflation of class probabilities. We evaluate robotic activity classification methods on a simulated OpenAI Gym environment and a real-world DESK dataset for surgical robotics. We observe that our proposed MLN-based approaches boost the performance of LSTM-based networks. In particular, MLN boosts the accuracy of LSTM from 71% to 84% on the Gym dataset and from 68% to 72% on the Taurus robot dataset. Furthermore, MLN (i.e., PriorLayer) shows regularization capability where it improves accuracy in initial LSTM training while avoiding over-fitting early, thus improves the final classification accuracy on unseen data. The code is available at https://github.com/masud99r/prediction-with-logic-constraints.