{'fraction_non_alphanumeric': 0.014659685863874346, 'fraction_numerical': 0.0, 'mean_word_length': 5.5479452054794525, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Probabilistic state verification for snap assemblies using the relative-change-based hierarchical taxonomy

Autonomous snap assemblies is a highly desirable robotic functionality. While much work has been done in active sensing for peg-in-hole assemblies and general compliant motions, snap assembly state estimation remains an open research problem. This work presents a probabilistic framework designed to account for uncertainties in assembly and yield more intuitive and robust outcome assessments. Simulation of an anthropomorphic robot HIRO performed a cantilever-snap assembly using the Pivot Approach control strategy and our snap verification system. The latter used a Bayesian Filter within its hierarchical taxonomy yielding belief states at two levels of the taxonomy. The last layer of the system, effectively assessed the outcomes of all test assemblies. The framework was effective in correctly assessing the outcome of all test assemblies.