☆ 4.6 Article

STL2vec: Signal Temporal Logic Embeddings for Control Synthesis With Recurrent Neural Networks

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints

Wenliang Liu et al.

Summary: This paper proposes a framework based on Recurrent Neural Networks (RNNs) to determine an optimal control strategy for a discrete-time system that satisfies Signal Temporal Logic (STL) formulae.

IEEE CONTROL SYSTEMS LETTERS (2022)

添加到收藏夹

Article Robotics

Learning From Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains

Aniruddh Gopinath Puranic et al.

Summary: Learning control policies that are safe, robust and interpretable is a major challenge in developing robotic systems. Using formal logic in learning-from-demonstrations is a new paradigm in reinforcement learning to estimate rewards and extract robot control policies to overcome these challenges. The approach involves inferring rewards automatically from user demonstrations based on specified logic, ranking them to determine optimal control policies.

IEEE ROBOTICS AND AUTOMATION LETTERS (2021)

添加到收藏夹

Article Computer Science, Hardware & Architecture

Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers: A Lagrange Multipliers Approach

Shakiba Yaghoubi et al.

ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS (2019)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Structured Reward Shaping using Signal Temporal Logic specifications

Anand Balakrishnan et al.

2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (2019)

添加到收藏夹

STL2vec: Signal Temporal Logic Embeddings for Control Synthesis With Recurrent Neural Networks

相关参考文献

Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints

Learning From Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains

Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers: A Lagrange Multipliers Approach

Structured Reward Shaping using Signal Temporal Logic specifications

导出引文

分享论文