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

TL;DR

This paper introduces a novel social path planner for bipedal robots that ensures safe and socially acceptable navigation in crowded environments by integrating signal temporal logic with learning-based planning.

Contribution

It presents a new social navigation approach for bipedal robots using a signal-temporal-logic-driven planner combined with deep learning from human crowd data.

Findings

Successfully integrates social path planning with full-body control in simulation.

Ensures safety and social norm compliance through formal logic specifications.

Demonstrates effective navigation in dynamic crowded scenarios.

Abstract

Social navigation for bipedal robots remains relatively unexplored due to the highly complex, nonlinear dynamics of bipedal locomotion. This study presents a preliminary exploration of social navigation for bipedal robots in a human crowded environment. We propose a social path planner that ensures the locomotion safety of the bipedal robot while navigating under a social norm. The proposed planner leverages a conditional variational autoencoder architecture and learns from human crowd datasets to produce a socially acceptable path plan. Robot-specific locomotion safety is formally enforced by incorporating signal temporal logic specifications during the learning process. We demonstrate the integration of the social path planner with a model predictive controller and a low-level passivity controller to enable comprehensive full-body joint control of Digit in a dynamic simulation.