Conflict-Directed Diverse Planning for Logic-Geometric Programming

Abstract

Robots operating in the real world must combine task planning for reasoning about what to do with motion planning for reasoning about how to do it -- this is known as task and motion planning. One promising approach for task and motion planning is Logic Geometric Programming (LGP) which integrates a logical layer and a geometric layer in an optimization formulation. The logical layer describes feasible high-level actions at an abstract symbolic level, while the geometric layer uses continuous optimization methods to reason about motion trajectories with geometric constraints. In this paper we propose a new approach for solving task and motion planning problems in the LGP formulation, that leverages state-of-the-art diverse planning at the logical layer to explore the space of feasible logical plans, and minimizes the number of optimization problems to be solved on the continuous geometric layer. To this end, geometric infeasibility is fed back into planning by identifying prefix conflicts and incorporating this back into the planner through a novel multi-prefix forbidding compilation. We further leverage diverse planning with a new novelty criteria for selecting candidate plans based on the prefix novelty, and a metareasoning approach which attempts to extract only useful conflicts by leveraging the information that is gathered in the course of solving the given problem.

A Conflict-Driven Interface Between Symbolic Planning and Nonlinear Constraint Solving

“…Our method provides an optimization-based formulation of TAMP [16], [17], [18], [19] which leverages nonlinear optimization to jointly compute a motion that satisfies all geometric and physical constraints. In contrast to previous solvers for LGP [18], namely Multi-Bound Tree Search [20], our solver provides a more efficient logic-geometric interface based on detecting infeasible subsets of constraints instead of infeasible action sequences, as our evaluations show.…”