path-frontier_skill

Overview

This skill enumerates nearby frontier positions reachable within a bounded number of navigation actions using a conservative BFS simulation. It returns an over-approximation of positions reachable from the start without executing any real movement, helping planners reason about possible next locations.

How this skill works

The skill runs a bounded breadth-first search over allowed nav actions (move, climb, descend, turn left/right) using nav_simulation.simulate_nav_step and the current spatial_map from world_state("spatial_map"). It treats unknown cells as traversable by default (allow_unknown=True) and reports reachable offsets as dx/dz plus the simulated action path. The starting cell is excluded and nav-turn counts as an action while not changing position.

When to use it

• To estimate which nearby positions a robot/agent could reach within a small number of navigation steps.
• During high-level planning to identify candidate next goals without committing to movement.
• When testing map coverage or exploring possible frontiers after a map update.
• To verify reachability constraints before issuing real navigation commands.
• When you need a conservative, simulation-only approximation that tolerates unknown cells.

Best practices

• Keep max_actions small (default 4) to limit combinatorial explosion and keep results meaningful.
• Ensure the spatial_map is populated (use mc-map-update) so the simulation has accurate cell data.
• Set allow_unknown=False for stricter, pessimistic reachability when unknowns should be treated as obstacles.
• Treat returned paths as estimates; they may fail in real execution and should be validated by an actual navigator.
• Expect zero results as a valid outcome; zero reachable positions does not indicate an error.

Example use cases

• Compute candidate nearby goals for a short-horizon exploration policy before selecting one to navigate to.
• Validate whether a proposed short path is plausible given current map knowledge without moving the agent.
• Report possible reachable offsets to a UI to visualize local frontiers around the agent.
• Use in planning simulations where actions must be counted (including turns) to estimate action budgets.