STRIPS Planning in Infinite Domains

نویسندگان

  • Caelan Reed Garrett
  • Tomás Lozano-Pérez
  • Leslie Pack Kaelbling
چکیده

Many robotic planning applications involve continuous actions with highly non-linear constraints, which cannot be modeled using modern planners that construct a propositional representation. We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints. The outputs of these generators interact with actions through possibly infinite streams of objects and static predicates. We provide two algorithms which both reduce STRIPStream problems to a sequence of finitedomain planning problems. The representation and algorithms are entirely domain independent. We demonstrate our framework on simple illustrative domains, and then on a high-dimensional, continuous robotic task and motion planning domain.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Planning Heuristic Based on Causal Graph Analysis

In recent years, heuristic search methods for classical planning have achieved remarkable results. Their most successful representative, the FF algorithm, performs well over a wide spectrum of planning domains and still sets the state of the art for STRIPS planning. However, there are some planning domains in which algorithms like FF and HSP perform poorly because their relaxation method of ign...

متن کامل

Domain-Independent Online Planning for STRIPS Domains

SimPlanner is an integrated tool for planning and executionmonitoring which allows to interleave planning and execution. In this paper we present the on-line planner incorporated in SimPlanner. This is a domain-independent planner for STRIPS domains. SimPlanner participated in the IPC 2002, obtaining very competitive results.

متن کامل

Learning STRIPS Operators from Noisy and Incomplete Observations

Agents learning to act autonomously in realworld domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing approaches cannot learn from noisy, incomplete observations typical of real-wo...

متن کامل

Planning as Satisfiability in Nondeterministic Domains

We focus on planning as satisfiability in simple nondeterministic domains. By “simple” we mean specified in a simple extension to the STRIPS formalism allowing for specifying actions with nondeterministic effects. This allows us to simplify and extend the theory presented in (Giunchiglia 2000). The result is a planning system which, in simple nondeterministic domains, is competitive with other ...

متن کامل

Learning Probabilistic Relational Planning Rules

To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPS-like planning operators from examples. We demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • CoRR

دوره abs/1701.00287  شماره 

صفحات  -

تاریخ انتشار 2017