Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation

Understand

In mobile manipulation (MM), robots can both navigate within and interact with their environment and are thus able to complete many more tasks than robots only capable of navigation or manipulation.

• In this work, we explore how to apply imitation learning (IL) to learn continuous visuo-motor policies for MM tasks.
• Much prior work has shown that IL can train visuo-motor policies for either manipulation or navigation domains, but few works have applied IL to the MM domain.
• Doing this is challenging for two reasons: on the data side, current interfaces make collecting high-quality human demonstrations difficult, and on the learning side, policies trained on limited data can suffer from covariate shift when deployed.