4 citations · 6 across the 9 of their papers we have counts for
11 papers
MM-LMPC: Multi-Modal Learning Model Predictive Control via Mode-Specific Terminal Design and Bandit-Based Exploration
Wataru Hashimoto, Kazumune Hashimoto, Masako Kishida
Learning Model Predictive Control (LMPC) improves iterative control tasks by using previous executions to construct the terminal constraint and terminal cost of the MPC problem. Al…
Forecast-Ensemble-Based Active Binary-Threshold Query Design for Interval Data Assimilation
Wataru Hashimoto, Kazumune Hashimoto, Haru Kuroki
Data assimilation estimates the evolving state of a dynamical system by combining model forecasts with observations. While many conventional methods assume point-valued measurement…
MM-LMPC: Multi-Modal Learning Model Predictive Control via Bandit-Based Mode Selection
Wataru Hashimoto, Kazumune Hashimoto
Learning Model Predictive Control (LMPC) improves performance on iterative tasks by leveraging data from previous executions. At each iteration, LMPC constructs a sampled safe set…
Reference-Free Iterative Learning Model Predictive Control with Neural Certificates
Wataru Hashimoto, Kazumune Hashimoto, Masako Kishida +1
In this paper, we propose a novel reference-free iterative learning model predictive control (MPC). In the proposed method, a certificate function based on the concept of Control L…
Clustering-based Recurrent Neural Network Controller synthesis under Signal Temporal Logic Specifications
Kazunobu Serizawa, Kazumune Hashimoto, Wataru Hashimoto +2
Autonomous robotic systems require advanced control frameworks to achieve complex temporal objectives that extend beyond conventional stability and trajectory tracking. Signal Temp…
Long-term Safe Reinforcement Learning with Binary Feedback
Akifumi Wachi, Wataru Hashimoto, Kazumune Hashimoto
Safety is an indispensable requirement for applying reinforcement learning (RL) to real problems. Although there has been a surge of safe RL algorithms proposed in recent years, mo…
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
Akifumi Wachi, Wataru Hashimoto, Xun Shen +1
Safe exploration is essential for the practical use of reinforcement learning (RL) in many real-world scenarios. In this paper, we present a generalized safe exploration (GSE) prob…
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors
Wataru Hashimoto, Kazumune Hashimoto, Akifumi Wachi +3
In this paper, we consider a way to safely navigate the robots in unknown environments using measurement data from sensory devices. The control barrier function (CBF) is one of the…
Neural Controller Synthesis for Signal Temporal Logic Specifications Using Encoder-Decoder Structured Networks
Wataru Hashimoto, Kazumune Hashimoto, Masako Kishida +1
In this paper, we propose a control synthesis method for signal temporal logic (STL) specifications with neural networks (NNs). Most of the previous works consider training a contr…
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes
Wang Zhijun, Kazumune Hashimoto, Wataru Hashimoto +1
This paper investigates the design of self-triggered control for networked control systems (NCS), where the dynamics of the plant is unknown apriori. To deal with the nature of the…
STL2vec: Signal Temporal Logic Embeddings for Control Synthesis With Recurrent Neural Networks
Wataru Hashimoto, Kazumune Hashimoto, Shigemasa Takai
In this paper, a method for learning a recurrent neural network (RNN) controller that maximizes the robustness of signal temporal logic (STL) specifications is presented. In contra…