5 research outputs found
Interaction Templates for Multi-Robot Systems
This work describes a framework for multi-robot problems that require or utilize interactions between robots. Solutions consider interactions on a motion planning level to determine the feasibility and cost of the multi-robot team solution. Modeling these problems with current integrated task and motion planning (TMP) approaches typically requires reasoning about the possible interactions and checking many of the possible robot combinations when searching for a solution.
We present a multi-robot planning method called Interaction Templates (ITs) which moves certain types of robot interactions from the task planner to the motion planner. ITs model interactions between a set of robots with a small roadmap. This roadmap is then tiled into the environment and connected to the robots’ individual roadmaps. The resulting combined roadmap allows interactions to be considered by the motion planner. We apply ITs to homogeneous and heterogeneous robot teams under both required and optional cooperation scenarios which previously required a task planning method. We show improved performance over a current TMP planning approach
Online Replanning in Belief Space for Partially Observable Task and Motion Problems
To solve multi-step manipulation tasks in the real world, an autonomous robot
must take actions to observe its environment and react to unexpected
observations. This may require opening a drawer to observe its contents or
moving an object out of the way to examine the space behind it. Upon receiving
a new observation, the robot must update its belief about the world and compute
a new plan of action. In this work, we present an online planning and execution
system for robots faced with these challenges. We perform deterministic
cost-sensitive planning in the space of hybrid belief states to select
likely-to-succeed observation actions and continuous control actions. After
execution and observation, we replan using our new state estimate. We initially
enforce that planner reuses the structure of the unexecuted tail of the last
plan. This both improves planning efficiency and ensures that the overall
policy does not undo its progress towards achieving the goal. Our approach is
able to efficiently solve partially observable problems both in simulation and
in a real-world kitchen.Comment: IEEE International Conference on Robotics and Automation (ICRA), 202