{'fraction_non_alphanumeric': 0.018901358535144713, 'fraction_numerical': 0.0, 'mean_word_length': 5.591439688715953, '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}}
Robust Planning with Emergent Human-like Behavior for Agents Traveling in Groups

To enable robots to smoothly interact with humans during their travels together as a group, robots need the ability to adapt their motions under environmental changes and ensure all group members’ routes are feasible. To achieve this ability, robots require knowledge of the final destination and the subgoals in between. In practice, such information is seldom shared explicitly among group members, and may be frequently updated. Under this uncertain setting, maintaining travel efficiency and behavior appropriateness becomes a challenge. Previous literature approached the problem by generating compliant coordinating motions inspired by human groups, with subgoal uncertainty remaining isolated from the plan evaluation process. We show that such coordination can lead the robot to "bad" transient states where inefficient planning and lost tracking may incur. We propose to resolve the problem by formulating the coordinating motion as a Bayesian stochastic game, to plan for the robot as a group member, in the meanwhile considering the long-term effect of uncertainty during path coordination. We show that the approach improves travel efficiency and partner tracking robustness, by preventing assertive decisions during the inference update process. Moreover, the approach presents "agency", in the sense that it can generate human-like motions, which can be applied and contribute to the pedestrian simulation literature; the approach also affords variants from the human-like motions to generate robot behaviors based on sensing capabilities, contributing to the methodology of robot behavior design.