FFRob: An Efficient Heuristic for Task and Motion Planning

Garrett, Caelan Reed; Lozano-Pérez, Tomás; Kaelbling, Leslie Pack

doi:10.1007/978-3-319-16595-0_11

Cited by 108 publications

(97 citation statements)

References 20 publications

(28 reference statements)

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“…Hence, we do not consider the use of, for example, specialised temporal reasoning in temporal planners (e.g., Halsey, Long, & Fox, 2003;Shin & Davis, 2005) to be in this category. However, one special case worth mention is the integration of geometric path or motion planning with task planning, which has been a focus of work in planning for robotics (Cambon, Gravot, & Alami, 2003;Cambon, Alami, & Gravot, 2009;Plaku & Hager, 2010;Lagriffoul, Dimitrov, Saffiotti, & Karlsson, 2012;Srivastava, Fang, Riano, Chitnis, Russel, & Abbeel, 2014;Toussaint, 2015;Garrett, Lozano-Pérez, & Kaelbling, 2015).…”

Section: Special-purpose Solvers In Heuristic Search Planningmentioning

confidence: 99%

Extending Classical Planning with State Constraints: Heuristics and Search for Optimal Planning

Haslum¹,

Ivankovic²,

Ramírez³

et al. 2018

jair

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We present a principled way of extending a classical AI planning formalism with systems of state constraints, which relate -sometimes determine -the values of variables in each state traversed by the plan. This extension occupies an attractive middle ground between expressivity and complexity. It enables modelling a new range of problems, as well as formulating more efficient models of classical planning problems. An example of the former is planning-based control of networked physical systems -power networks, for examplein which a local, discrete control action can have global effects on continuous quantities, such as altering flows across the entire network. At the same time, our extension remains decidable as long as the satisfiability of sets of state constraints is decidable, including in the presence of numeric state variables, and we demonstrate that effective techniques for costoptimal planning known in the classical setting -in particular, relaxation-based admissible heuristics -can be adapted to the extended formalism. In this paper, we apply our approach to constraints in the form of linear or non-linear equations over numeric state variables, but the approach is independent of the type of state constraints, as long as there exists a procedure that decides their consistency. The planner and the constraint solver interact through a well-defined, narrow interface, in which the solver requires no specialisation to the planning context. Furthermore, we present an admissible search algorithm -a variant of A -that is able to make use of additional information provided by the search heuristic, in the form of preferred actions. Although preferred actions have been widely used in satisficing planning, we are not aware of any previous use of them in optimal planning.

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Section: Special-purpose Solvers In Heuristic Search Planningmentioning

confidence: 99%

Extending Classical Planning with State Constraints: Heuristics and Search for Optimal Planning

Haslum¹,

Ivankovic²,

Ramírez³

et al. 2018

jair

View full text Add to dashboard Cite

show abstract

“…The FFRob algorithm of Garrett et al [14,16] is related to the INCREMENTAL algorithm discussed in this paper; it also involves sampling a fixed set of object poses and robot configurations and then planning with them. An iterative version of FFRob is probabilistically complete and exponentially convergent [16].…”

Section: Related Workmentioning

confidence: 99%

Sample-Based Methods for Factored Task and Motion Planning

Garrett¹,

Lozano-Pérez²,

Kaelbling³

2017

Robotics: Science and Systems XIII

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Abstract-There has been a great deal of progress in developing probabilistically complete methods that move beyond motion planning to multi-modal problems including various forms of task planning. This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and action spaces. The formulation characterizes conditions on the submanifolds in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that are provided as part of a domain specification. We present domain-independent sample-based planning algorithms and show that they are both probabilistically complete and computationally efficient on a set of challenging benchmark problems.

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“…Efficient integration of high-level, symbolic task planning and lowlevel, geometric motion planning is difficult; recent research has proposed several approaches [1], [2], [3], [4], [5], [6]. In this paper, we adopt the abstraction framework developed by Srivastava et al [1] (henceforth referred to as to factor the reasoning and search problems into interacting logical and geometric components.…”

Section: Introductionmentioning

confidence: 99%

Guided search for task and motion plans using learned heuristics

Chitnis

Hadfield-Menell²,

Gupta

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Tasks in mobile manipulation planning often require thousands of individual motions to complete. Such tasks require reasoning about complex goals as well as the feasibility of movements in configuration space. In discrete representations, planning complexity is exponential in the length of the plan. In mobile manipulation, parameters for an action often draw from a continuous space, so we must also cope with an infinite branching factor. Task and motion planning (TAMP) methods integrate logical search over high-level actions with geometric reasoning to address this challenge. We present an algorithm that searches the space of possible task and motion plans and uses statistical machine learning to guide the search process. Our contributions are as follows: 1) we present a complete algorithm for TAMP; 2) we present a randomized local search algorithm for plan refinement that is easily formulated as a Markov decision process (MDP); 3) we apply reinforcement learning (RL) to learn a policy for this MDP; 4) we learn from expert demonstrations to efficiently search the space of highlevel task plans, given options that address different (potential) infeasibilities; and 5) we run experiments to evaluate our system in a variety of simulated domains. We show significant improvements in performance over prior work.

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FFRob: An Efficient Heuristic for Task and Motion Planning

Cited by 108 publications

References 20 publications

Extending Classical Planning with State Constraints: Heuristics and Search for Optimal Planning

Extending Classical Planning with State Constraints: Heuristics and Search for Optimal Planning

Sample-Based Methods for Factored Task and Motion Planning

Guided search for task and motion plans using learned heuristics

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