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Found 13 papers, 6 papers with code
Hierarchical Deep Learning for Intention Estimation of Teleoperation Manipulation in Assembly Tasks
We implement the algorithm on a virtual reality (VR) setup to teleoperate robotic hands in a simulation with various assembly tasks to show the effectiveness of online estimation.
Control Barrier Function for Linearizable Systems with High Relative Degrees from Signal Temporal Logics: A Reference Governor Approach
We developed an explicit reference governor-guided control barrier function (ERG-guided CBF) method that enables the application of first-order CBFs to high-order linearizable systems.
Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments
We formulate motion planning as a probabilistic-labeled partially observable Markov decision process (PL-POMDP) problem and use linear temporal logic (LTL) to express the complex task.
Learning Signal Temporal Logic through Neural Network for Interpretable Classification
Machine learning techniques using neural networks have achieved promising success for time-series data classification.
Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications
This is achieved by decomposing an infeasible LTL formula into several reach-avoid sub-tasks with shorter horizons, which can be trained in a modular DRL architecture.
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions
We demonstrate that using parallel branches as opposed to serial stacked convolutional layers will be a more powerful design for robotic visual grasping tasks.
Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications
This work presents a deep policy gradient algorithm for controlling a robot with unknown dynamics operating in a cluttered environment when the task is specified as a Linear Temporal Logic (LTL) formula.
Time-Incremental Learning from Data Using Temporal Logics
In this paper, we introduce a time-incremental learning framework: given a dataset of labeled signal traces with a common time horizon, we propose a method to predict the label of a signal that is received incrementally over time, referred to as prefix signal.
Intelligent Traffic Light via Policy-based Deep Reinforcement Learning
At first, the obtained optimal policy from PPO is compared to those from DQN and DDQN.
Safety-Critical Learning of Robot Control with Temporal Logic Specifications
Then, by applying a reward shaping technique, we develop a modular policy-gradient architecture exploiting the benefits of the automaton structure to decompose overall tasks and enhance the performance of learned controllers; (3) by incorporating Gaussian Processes (GPs) to estimate the uncertain dynamic systems, we synthesize a model-based safe exploration during the learning process using Exponential Control Barrier Functions (ECBFs) that generalize systems with high-order relative degrees; (4) to further improve the efficiency of exploration, we utilize the properties of LTL automata and ECBFs to propose a safe guiding process.
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic
This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state and action spaces.
Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata
This paper studies the control synthesis of motion planning subject to uncertainties.
Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfaction
This paper presents a model-free reinforcement learning (RL) algorithm to synthesize a control policy that maximizes the satisfaction probability of linear temporal logic (LTL) specifications.
