Automatic Extension of a Symbolic Mobile Manipulation Skill Set
This addresses the problem of limited adaptability in symbolic planning for non-expert users operating autonomous robots, representing an incremental improvement.
The paper tackles the problem of symbolic planners being unable to adapt to environmental or task changes not captured in initial descriptions by proposing a method for agents to automatically extend their skill sets. The result shows a 29% higher success rate and 68% faster runtime compared to a Monte Carlo Tree Search baseline in simulation on object rearrangement tasks.
Symbolic planning can provide an intuitive interface for non-expert users to operate autonomous robots by abstracting away much of the low-level programming. However, symbolic planners assume that the initially provided abstract domain and problem descriptions are closed and complete. This means that they are fundamentally unable to adapt to changes in the environment or task that are not captured by the initial description. We propose a method that allows an agent to automatically extend its skill set, and thus the abstract description, upon encountering such a situation. We introduce strategies for generalizing from previous experience, completing sequences of key actions and discovering preconditions to ensure the efficiency of our skill sequence exploration scheme. The resulting system is evaluated in simulation on object rearrangement tasks. Compared to a Monte Carlo Tree Search baseline, our strategies for efficient search have on average a 29% higher success rate at a 68% faster runtime.