# Pushing the Boundaries of Asymptotic Optimality in Integrated Task and Motion Planning

## TL;DR

This paper advances integrated task and motion planning by demonstrating that asymptotic optimality can be achieved with standard connection radii even when transition sets are non-smooth, broadening the applicability of AO algorithms.

## Contribution

It generalizes the concept of clearance in task and motion planning to include non-smooth transition states, maintaining asymptotic optimality without increasing connection radius.

## Key findings

- Asymptotic optimality holds with standard connection radius for non-smooth transitions.
- Generalized clearance concept includes non-smooth boundary points.
- AO solutions are achievable in more realistic, complex planning scenarios.

## Abstract

Integrated task and motion planning problems describe a multi-modal state space, which is often abstracted as a set of smooth manifolds that are connected via sets of transitions states. One approach to solving such problems is to sample reachable states in each of the manifolds, while simultaneously sampling transition states. Prior work has shown that in order to achieve asymptotically optimal (AO) solutions for such piecewise-smooth task planning problems, it is sufficient to double the connection radius required for AO sampling-based motion planning. This was shown under the assumption that the transition sets themselves are smooth. The current work builds upon this result and demonstrates that it is sufficient to use the same connection radius as for standard AO motion planning. Furthermore, the current work studies the case that the transition sets are non-smooth boundary points of the valid state space, which is frequently the case in practice, such as when a gripper grasps an object. This paper generalizes the notion of clearance that is typically assumed in motion and task planning to include such individual, potentially non-smooth transition states. It is shown that asymptotic optimality is retained under this generalized regime.