Safe and Interaction-Aware Local Motion Planning in Human-Centered Environments

Abstract

Autonomous mobile robots, once confined to structured settings like warehouses, now operate in dynamic, human-centered environments such as hospitals and streets, where interactions with humans are unavoidable. This shift poses major challenges for local motion planning, requiring robots to navigate complex environments where human behavior is difficult to predict while prioritizing safety, adaptability, and social compliance. This thesis addresses these challenges by proposing solutions for interaction-aware, safe, socially compliant, and adaptive planning.

We first examine the standard approach of splitting local motion planning into two sub-problems: predicting human trajectories and solving constrained trajectory optimization to avoid collisions. While this approach provides safety guarantees and can handle uncertainties, it neglects the interactions between the robot and the humans. We address this by formulating a Model Predictive Control problem in which the robot’s action influences both its own state and the human states. Focusing on navigation among pedestrians, we leverage the interpretable and established Social Force Model (SFM) to model the human response dynamics to robot actions. By accounting for the robot’s influence on pedestrian behavior, we demonstrate that the robot can guide pedestrian behavior. However, a carefully designed cost function is essential to promote cooperation and prevent exploitation.

Next, we focus on the limitations of learning-based approaches that can address social compliance, specifically, Imitation Learning (IL). While IL enables the learning of socially compliant behaviors from demonstrations or observations, the resulting policy lacks formal safety guarantees. Safety filters based on, e.g., Control Barrier Functions (CBFs), adapt control inputs to ensure safety and can be combined with IL policies. While CBFs are an effective tool to certify safety, two challenges remain: constructing them for complex systems with input constraints and accounting for model uncertainties. We propose Robust Policy Control Barrier Functions, a method for constructing robust CBFs that guarantees safety under worst-case bounded disturbances. Furthermore, we present a practical approximation and demonstrate its effectiveness in simulation and hardware experiments.

Next, we focus on adaptability, specifically in data-driven pedestrian prediction models, which are crucial for the decoupled prediction and planning approach. While existing models are trained offline on general datasets, they may not reflect the behavior of pedestrians in the robot’s environment. To address this, we propose a self-supervised continual learning framework that refines models during deployment using online data from the robot’s perception pipeline, preserving prior knowledge through regularization and selective retraining. Experiments show improved performance compared to naive online training. Although we focus on pedestrian prediction, the approach could extend to other methods applying learning-from-observations.

In summary, this thesis integrates interaction awareness into decoupled motion planning by leveraging the established SFM, presents a method for constructing safety filters using practical approximations of robust CBFs, and develops a framework that addresses adaptability through self-supervised continual learning. Integrating the safety filter with a socially compliant, interaction-aware policy learned from observations and adapted online through continual learning offers a comprehensive solution.
This combination could pave the way for local motion planning that is interaction-aware, safe, socially compliant, and adaptive.