Media Summary: Presented at CORL 2022 (Oral Session) Authors: Yanwei Wang, Nadia Figueroa, Shen Li, Ankit Shah, Julie Shah Abstract: ... MIT 16.412J Cognitive Robotics, Spring 2016 View the complete course: Instructor: MIT students ... A recording of a talk held by Jana Tumova in the IRLab seminar series at University of Birmingham on 04 June 2021.

Temporal Logic Imitation Learning Plan - Detailed Analysis & Overview

Presented at CORL 2022 (Oral Session) Authors: Yanwei Wang, Nadia Figueroa, Shen Li, Ankit Shah, Julie Shah Abstract: ... MIT 16.412J Cognitive Robotics, Spring 2016 View the complete course: Instructor: MIT students ... A recording of a talk held by Jana Tumova in the IRLab seminar series at University of Birmingham on 04 June 2021. Project website (paper, code, video): Abstract: Long-horizon robot manipulation policies trained ... Elaborating on Learned Demonstrations with Leslie Lamport, winner of the Association for Computing Machinery's A.M. Turing Award, discuses his

Moshe Vardi, Professor at Rice University and one of the most influential figures in ICRA'20 presentation of the CPSL paper "Deep

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Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations
Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations
Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains
Advanced 6. Planning with Temporal Logic
Jana Tumova - Motion planning with temporal logic tasks and constraints
STL: Signal Temporal Logic
Temporal Self-Imitation Learning
Learning temporal logic formulas from suboptimal demonstrations: theory and experiments
RSS 2020, Spotlight Talk 4: Elaborating on Learned Demonstrations with Temporal Logic Specifications
Lamport on Temporal Logic of Actions and refinement mapping
Symbolic Linear Temporal Logic over Finite Traces Synthesis - Moshe Vardi
Deep Imitative Reinforcement Learning for Motion Planning with Noisy Semantic Observations
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Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations

Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations

Learning

Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations

Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations

Presented at CORL 2022 (Oral Session) Authors: Yanwei Wang, Nadia Figueroa, Shen Li, Ankit Shah, Julie Shah Abstract: ...

Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains

Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains

Follow The Rules: Online Signal

Advanced 6. Planning with Temporal Logic

Advanced 6. Planning with Temporal Logic

MIT 16.412J Cognitive Robotics, Spring 2016 View the complete course: https://ocw.mit.edu/16-412JS16 Instructor: MIT students ...

Jana Tumova - Motion planning with temporal logic tasks and constraints

Jana Tumova - Motion planning with temporal logic tasks and constraints

A recording of a talk held by Jana Tumova in the IRLab seminar series at University of Birmingham on 04 June 2021.

STL: Signal Temporal Logic

STL: Signal Temporal Logic

STL, Signal

Temporal Self-Imitation Learning

Temporal Self-Imitation Learning

Project website (paper, code, video): http://generalroboticslab.com/TSIL Abstract: Long-horizon robot manipulation policies trained ...

Learning temporal logic formulas from suboptimal demonstrations: theory and experiments

Learning temporal logic formulas from suboptimal demonstrations: theory and experiments

Video accompanying the paper "

RSS 2020, Spotlight Talk 4: Elaborating on Learned Demonstrations with Temporal Logic Specifications

RSS 2020, Spotlight Talk 4: Elaborating on Learned Demonstrations with Temporal Logic Specifications

Elaborating on Learned Demonstrations with

Lamport on Temporal Logic of Actions and refinement mapping

Lamport on Temporal Logic of Actions and refinement mapping

Leslie Lamport, winner of the Association for Computing Machinery's A.M. Turing Award, discuses his

Symbolic Linear Temporal Logic over Finite Traces Synthesis - Moshe Vardi

Symbolic Linear Temporal Logic over Finite Traces Synthesis - Moshe Vardi

Moshe Vardi, Professor at Rice University and one of the most influential figures in

Deep Imitative Reinforcement Learning for Motion Planning with Noisy Semantic Observations

Deep Imitative Reinforcement Learning for Motion Planning with Noisy Semantic Observations

ICRA'20 presentation of the CPSL@Duke paper "Deep

Signal Temporal Logic Neural Predictive Control (Supplementary video)

Signal Temporal Logic Neural Predictive Control (Supplementary video)

Paper title: Signal