2021/09/12 by Maks Sorokin, Jie Tan, Sorokin, Maks +5 · 3 citations
Computer Science · Engineering · #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization #cs.RO
paper · pdf · doi:10.48550/arxiv.2109.05603
Submitted to IEEE Robotics and Automation Letters (RA-L)
arxiv created 2021/09/12 · openalex publication_date 2021/09/12 · arxiv updated 2021/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Outdoor navigation on sidewalks in urban environments is the key technology behind important human assistive applications, such as last-mile delivery or neighborhood patrol. This paper aims to develop a quadruped robot that follows a route plan generated by public map services, while remaining on sidewalks and avoiding collisions with obstacles and pedestrians. We devise a two-staged learning framework, which first trains a teacher agent in an abstract world with privileged ground-truth information, and then applies Behavior Cloning to teach the skills to a student agent who only has access to realistic sensors. The main research effort of this paper focuses on overcoming challenges when deploying the student policy on a quadruped robot in the real world. We propose methodologies for designing sensing modalities, network architectures, and training procedures to enable zero-shot policy transfer to unstructured and dynamic real outdoor environments. We evaluate our learning framework on a quadrupedal robot navigating sidewalks in the city of Atlanta, USA. Using the learned navigation policy and its onboard sensors, the robot is able to walk 3.2 kilometers with a limited number of human interventions.