9 citations · 36 across the 11 of their papers we have counts for
8 papers · 1 filter
Deep Reactive Planning in Dynamic Environments
Kei Ota, Devesh K. Jha, Tadashi Onishi +5
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution. While goal conditi…
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
Kei Ota, Devesh K. Jha, Diego Romeres +7
Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success…
Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks
Yifang Liu, Diego Romeres, Devesh K. Jha +1
One of the main challenges in peg-in-a-hole (PiH) insertion tasks is in handling the uncertainty in the location of the target hole. In order to address it, high-dimensional sensor…
A Holistic Framework for Parameter Coordination of Interconnected Microgrids against Disasters
Tong Huang, Hongbo Sun, Kyeong Jin Kim +2
This paper proposes a holistic framework for parameter coordination of a power electronic-interfaced microgrid interconnection against natural disasters. The paper identifies a tra…
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Kei Ota, Tomoaki Oiki, Devesh K. Jha +2
Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training lar…
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements
Alberto Dalla Libera, Diego Romeres, Devesh K. Jha +2
In this paper, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR). In many mechanical syste…