Automated Abstraction of Manipulation Domains for Cost-Based Reactive Synthesis

He, Keliang; Lahijanian, Morteza; Kavraki, Lydia E.; Vardi, Moshe Y.

doi:10.1109/lra.2018.2889191

Cited by 19 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LTL of finite traces (LTL f ) (De Giacomo and Vardi, 2013) and co-safe LTL (scLTL) (Kupferman and Vardi, 2001) are an efficient fragment of LTL designed to analyze and verify finite properties. Since robotic tasks usually have a defined ending criterion, LTL f and scLTL are well suited as a specification language in task planning (Wells et al, 2020;He et al, 2019He et al, , 2018He et al, , 2015Vasilopoulos et al, 2021;Lacerda et al, 2014;Schillinger et al, 2018). All classical LTL definitions or fragments thereof rely on discrete time and Boolean predicates, but some define quantitative predicates (Li et al, 2017) or robustness metrics (Vasile et al, 2017;Tumova et al, 2013).…”

Section: Temporal Logics In Roboticsmentioning

confidence: 99%

SpaTiaL: Monitoring and Planning of Robotic Tasks Using Spatio-Temporal Logic Specifications

Pek

Schuppe

Esposito

et al. 2023

Preprint

View full text Add to dashboard Cite

Many tasks require robots to manipulate objects while satisfying a complex interplay of spatial and temporal constraints. For instance, a table setting robot first needs to place a mug and then fill it with coffee, while satisfying spatial relations such as forks need to placed left of plates. Existing solutions are often not robot-agnostic, not expressive enough to monitor and plan complex tasks, and require users to manually define object relations. We propose the spatio-temporal framework SpaTiaL that unifies the specification, monitoring, and planning of object-centric robotic tasks. SpaTiaL captures diverse spatial relations between objects and temporal task patterns. Our experiments with recorded data, simulations, and robots demonstrate how SpaTiaL provides real-time monitoring and facilitates online planning. SpaTiaL is open source and easily expandable to new object relations and robotic applications.

show abstract

Section: Temporal Logics In Roboticsmentioning

confidence: 99%

SpaTiaL: Monitoring and Planning of Robotic Tasks Using Spatio-Temporal Logic Specifications

Pek

Schuppe

Esposito

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Synthesis: Work in synthesis for robotics from temporal logic specifications (Kress-Gazit et al 2018) allows us to specify a reactive high-level task for a robot and produce either a strategy guaranteed to succeed, or a proof that the task cannot be accomplished (e.g. (Lahijanian et al 2012;He et al 2018;Wongpiromsarn et al 2010;DeCastro and Kress-Gazit 2016;Kress-Gazit et al 2009;He et al 2019)).…”

Section: Related Workmentioning

confidence: 99%

Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations

Pacheck¹,

James²,

Konidaris³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We further envision that our tools can be used for applications beyond ADAS/AV. For example, in robot manipulation problems where mission requirements are already described in temporal logic (e.g., see [31] and [32]).…”

Section: Introductionmentioning

confidence: 99%

Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic

Hekmat-Nejad¹,

Hoxha²,

Deshmukh³

et al. 2022

Preprint

View full text Add to dashboard Cite

Automated vehicles (AV) heavily depend on robust perception systems. Current methods for evaluating vision systems focus mainly on frame-by-frame performance. Such evaluation methods appear to be inadequate in assessing the performance of a perception subsystem when used within an AV. In this paper, we present a logic -referred to as Spatio-Temporal Perception Logic (STPL) -which utilizes both spatial and temporal modalities. STPL enables reasoning over perception data using spatial and temporal relations. One major advantage of STPL is that it facilitates basic sanity checks on the real-time performance of the perception system, even without ground-truth data in some cases. We identify a fragment of STPL which is efficiently monitorable offline in polynomial time. Finally, we present a range of specifications for AV perception systems to highlight the types of requirements that can be expressed and analyzed through offline monitoring with STPL.

show abstract

Automated Abstraction of Manipulation Domains for Cost-Based Reactive Synthesis

Cited by 19 publications

References 19 publications

SpaTiaL: Monitoring and Planning of Robotic Tasks Using Spatio-Temporal Logic Specifications

SpaTiaL: Monitoring and Planning of Robotic Tasks Using Spatio-Temporal Logic Specifications

Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations

Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic

Contact Info

Product

Resources

About