HATP: An HTN Planner for Robotics

This transition requires sv to have the value a at time ts (i.e. that the fluent h sv = a i holds at ts) and states that sv will have the value b at time te, meaning that the..

One difference between HTN planners and generative planners is on the way their plans are generated. HTN planners build plans by systematically decomposing all tasks while

The planning problem is then decomposed into two subproblems, as described in [19]; the algorithm searches for a trajectory of the main body of the robot, then contacts are

Now, if we introduce the following set of rules (avoid to have a bottle or glass put on the sofa, avoid to leave the cupboard open, avoid awkward sequences such that robot puts down

Combining HATP with geometric planning algorithms used in robotics is therefore crucial to be able to obtain primitive solutions that are viable in the real world.. To this end, we

By integrating these two allocation and planning paradigms, our ambition is to lay down the foundations of a principled approach that includes task dependencies and constraints to

Our current GTP implementation has some of the func- tionality depicted in Figure 3, including: (i) storing the se- quence of GTP tasks pursued so far; (ii) basic backtrack- ing to

Our current focus is not efficient temporal plan- ning per se, but the tight integration of acting and planning, which is addressed by: (i) extending HTN methods with re-