Learning Neuro-symbolic Programs for Language Guided Robot Manipulation

Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot. Prior approaches for this task possess one of the following limitations: (i) rely on hand-coded symbols for concepts limiting generalization beyond those seen during training [1] (ii) infer action sequences from instructions but require dense sub-goal supervision [2] or (iii) lack semantics required for deeper object-centric reasoning inherent in interpreting complex instructions [3]. In contrast, our approach can handle linguistic as well as perceptual variations, end-to-end trainable and requires no intermediate supervision. The proposed model uses symbolic reasoning constructs that operate on a latent neural object-centric representation, allowing for deeper reasoning over the input scene. Central to our approach is a modular structure consisting of a hierarchical instruction parser and an action simulator to learn disentangled action representations. Our experiments on a simulated environment with a 7-DOF manipulator, consisting of instructions with varying number of steps and scenes with different number of objects, demonstrate that our model is robust to such variations and significantly outperforms baselines, particularly in the generalization settings. The code, dataset and experiment videos are available at https://nsrmp.github.ioComment: International Conference on Robotics and Automation (ICRA), 202

Discordant dating of pregnancy by LMP and ultrasound and its implications in perinatal statistics

Context: High perinatal mortality in India may be caused by inaccurate dating of pregnancy resulting from suboptimal uptake of antenatal care and ultrasound services during pregnancy. Aim: To determine the discrepancy in the last menstrual period (LMP) assigned expected date of delivery (EDD) and ultrasound assigned EDD in pregnant women in a rural district of central India. Methods: Data from an ongoing cross-sectional screening program providing fetal radiology imaging in Guna district of Madhya Pradesh from 2012–2019 was analyzed for recall of LMP and discordance between LMP and ultrasound assigned EDD. The discrepancy was present when EDD assigned by ultrasound differed by 3 or more days at gestational ages less than 8+6 weeks, 5–7 days at gestational ages 8+6 weeks till 14 weeks, and 7–10 days at gestational ages 14–20 weeks. Results: The program screened 14,701 pregnant women of which 4,683 (31.8+6%, 95% CI: 31.11, 32.61) could not recall LMP. EDD assigned by LMP and ultrasound matched in 7,035 (70.22%, 95% CI: 69.32, 71.12) of the remaining 10,018 pregnant women. EDD was overestimated by LMP for 26.06% (95% CI: 25.21, 26.93) women; these foetuses were at risk of being misclassified as a term fetus. In 2018, the project had no maternal deaths, infant mortality rate of 24.7, low birth weight rate of 9.69%, and 100% antenatal coverage. Conclusion: Accurate dating of pregnancy and systematic follow-up integrating radiology imaging and obstetrics care for appropriate risk-based management of pregnant women can significantly improve perinatal statistics of India