Authors: Russell Mendonca, Emmanuel Panov, Bernadette Bucher, Jiuguang Wang, Deepak Pathak
Abstract: We present a fully autonomous real-world RL framework for mobile manipulation
that can learn policies without extensive instrumentation or human supervision.
This is enabled by 1) task-relevant autonomy, which guides exploration towards
object interactions and prevents stagnation near goal states, 2) efficient
policy learning by leveraging basic task knowledge in behavior priors, and 3)
formulating generic rewards that combine human-interpretable semantic
information with low-level, fine-grained observations. We demonstrate that our
approach allows Spot robots to continually improve their performance on a set
of four challenging mobile manipulation tasks, obtaining an average success
rate of 80% across tasks, a 3-4 improvement over existing approaches. Videos
can be found at https://continual-mobile-manip.github.io/
Source: http://arxiv.org/abs/2409.20568v1
