Learning Human-Aware Strategies for Legible and Predictable Robot Navigation

Abstract

A challenge in human-robot interaction, particularly in dynamic and crowded environments, is to design navigation that is both legible and predictable. For people to feel comfortable in the same environment as robots, their movements must be legible - ensuring quick understanding of the robot’s intentions - and predictable, meaning they align with human expectations.

In this thesis, we introduce a learning-based navigation system that leverages a vector reward function to capture the dual objectives of legibility and predictability. Rather than relying on manually designed transitioning rules or fixed weighting parameters such as alpha and beta, the reward function is learned from expert demonstrations generated by a planner (LPSNav) that blends between these objectives using a continuous parameter. Our approach does not attempt to replicate the planner’s handcrafted logic, but instead generalizes the emergent patterns in its trajectories through a supervised learning framework inspired by the structure of maximum entropy IRL.

The proposed architecture includes: an LSTM-based Robot Motion History Encoder, a CNN-based Environment Encoder, an LSTM-based Human History Encoder, and a two-channel Reward Analysis Engine.

The system was evaluated both in simulation using the nuScenes dataset and in real-world trials using the Clearpath Jackal robot. In the user study, participants interacted with the robot in predefined navigation scenarios, and their feedback was used to assess the perceived clarity and predictability of the robot’s actions.

Results show that our method generates more human-like trajectories and outperforms baseline models in scenarios requiring socially acceptable motion, such as intersections, sidestepping, and close-proximity passing. We provide both quantitative metrics (e.g., ADE and FDE) and qualitative visualizations demonstrating smooth and socially-aware navigation behavior.

This work represents a step toward safer and more intuitive human-robot coexistence, offering a practical solution for real-world robotic deployment.