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Social navigation for bipedal robots remains relatively unexplored due to thehighly complex, nonlinear dynamics of bipedal locomotion. This study presents apreliminary exploration of social navigation for bipedal robots in a humancrowded environment. We propose a social path planner that ensures thelocomotion safety of the bipedal robot while navigating under a social norm.The proposed planner leverages a conditional variational autoencoderarchitecture and learns from human crowd datasets to produce a sociallyacceptable path plan. Robot-specific locomotion safety is formally enforced byincorporating signal temporal logic specifications during the learning process.We demonstrate the integration of the social path planner with a modelpredictive controller and a low-level passivity controller to enablecomprehensive full-body joint control of Digit in a dynamic simulation.

Socially Acceptable Bipedal Navigation: A Signal-Temporal-Logic- Driven Approach for Safe Locomotion