Learning Contact-based Navigation in Crowds

by   Kyle Morgenstein, et al.

Navigation strategies that intentionally incorporate contact with humans (i.e. "contact-based" social navigation) in crowded environments are largely unexplored even though collision-free social navigation is a well studied problem. Traditional social navigation frameworks require the robot to stop suddenly or "freeze" whenever a collision is imminent. This paradigm poses two problems: 1) freezing while navigating a crowd may cause people to trip and fall over the robot, resulting in more harm than the collision itself, and 2) in very dense social environments where collisions are unavoidable, such a control scheme would render the robot unable to move and preclude the opportunity to study how humans incorporate robots into these environments. However, if robots are to be meaningfully included in crowded social spaces, such as busy streets, subways, stores, or other densely populated locales, there may not exist trajectories that can guarantee zero collisions. Thus, adoption of robots in these environments requires the development of minimally disruptive navigation plans that can safely plan for and respond to contacts. We propose a learning-based motion planner and control scheme to navigate dense social environments using safe contacts for an omnidirectional mobile robot. The planner is evaluated in simulation over 360 trials with crowd densities varying between 0.0 and 1.6 people per square meter. Our navigation scheme is able to use contact to safely navigate in crowds of higher density than has been previously reported, to our knowledge.


A Deep Learning Approach To Multi-Context Socially-Aware Navigation

We present a context classification pipeline to allow a robot to change ...

Robust Navigation of a Soft Growing Robot by Exploiting Contact with the Environment

Navigation and motion control of a robot to a destination are tasks that...

Following Social Groups: Socially Compliant Autonomous Navigation in Dense Crowds

In densely populated environments, socially compliant navigation is crit...

Game-theoretical trajectory planning enhances social acceptability for humans

Since humans and robots are increasingly sharing portions of their opera...

Potential Gap: Using Reactive Policies to Guarantee Safe Navigation

This paper considers the integration of gap-based local navigation metho...

Safer Gap: A Gap-based Local Planner for Safe Navigation with Nonholonomic Mobile Robots

This paper extends the gap-based navigation technique in Potential Gap b...

Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization

This paper presents a fully decentralized approach for realtime non-coop...

Please sign up or login with your details

Forgot password? Click here to reset