Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Harshal Maske"'
Publikováno v:
IEEE Control Systems. 41:30-69
Monitoring and modeling large-scale stochastic phenomena with both spatial and temporal (spatiotemporal) evolution by using a network of distributed sensors is a critical problem in many control applications (see "Summary"). Consider, for example, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a5070f815190f8dfe28cfe9ce2c88cc
https://doi.org/10.1109/mcs.2020.3032801
https://doi.org/10.1109/mcs.2020.3032801
Task Learning, Intent Prediction, and Adaptive Blended Shared Control With Application to Excavators
Publikováno v:
IEEE Transactions on Control Systems Technology. 29:18-28
Human operators of construction equipment usually exhibit strong situational awareness, which enables them to execute tasks while handling unexpected uncertainties and adapting to environmental changes. Automatic control, on the other hand, is genera
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63cb6cea0fdf7ece95fd7e0f41fa5f95
https://doi.org/10.1109/tcst.2019.2959536
https://doi.org/10.1109/tcst.2019.2959536
Publikováno v:
IFAC-PapersOnLine. 53:14936-14941
In this work, we introduce a scalable, decentralized deep reinforcement learning (DRL) scheme for controlling traffic signalization. The work builds on previous results using multi-agent DRL, with a new state representation and reward definitions. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::228d0b39e8bd7209bf28ee0c95230930
https://doi.org/10.1016/j.ifacol.2020.12.1980
https://doi.org/10.1016/j.ifacol.2020.12.1980
Publikováno v:
IFAC-PapersOnLine. 50:14928-14933
The situational awareness, robustness and expertise unique to skilled operators have precluded the full automation of many construction tasks. However, automatic control may be able to execute some quasi-repetitive construction tasks more efficiently
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::878d30d4ada323214a1210b736e54b4c
https://doi.org/10.1016/j.ifacol.2017.08.2541
https://doi.org/10.1016/j.ifacol.2017.08.2541
Publikováno v:
ITSC
In this work, we introduce a scalable, decentralized deep reinforcement learning (RL) scheme for optimizing vehicle traffic consisting of both autonomous and human-driven vehicles. The control inputs to the system are the following distance and lane
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bf0733e4328d445465948d3a294b873b
https://doi.org/10.1109/itsc.2019.8916776
https://doi.org/10.1109/itsc.2019.8916776
Autor:
Girish Chowdhary, Harshal Maske
Publikováno v:
CDC
Although Markov models are widely used and researched, improving their capability to guarantee optimal performance in real world processes relies on perfect state inference amidst non-stationarity. This paper develops a novel estimation technique to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::354b82e6e39f4e28a8a3606b75d0f845
https://doi.org/10.1109/cdc.2018.8619316
https://doi.org/10.1109/cdc.2018.8619316
Publikováno v:
CDC
Human operators of construction and farming equipment exhibit strong situational awareness which enables them to execute tasks while robustly handling unexpected uncertainties in the environment. Automatic control, on the other hand, is generally cap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12b1030d6fe1964d2a1d0b56a0d25699
https://doi.org/10.1109/cdc.2018.8619826
https://doi.org/10.1109/cdc.2018.8619826
Publikováno v:
SMC
Combining the benefits of robust situational awareness of human operators with the efficiency and precision of automatic control has been an important topic of human-machine shared control. The emphasis is on keeping human operators in the loop while
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::606955365bfaffc21fe007067944a13a
https://doi.org/10.1109/smc.2018.00465
https://doi.org/10.1109/smc.2018.00465
Publikováno v:
ACC
Finding spatial locations of physical sensors is critical to reliably estimating and monitoring spatiotemporal systems, such as weather, traffic, or social networks. Existing sensor placement approaches that leverage mutual information or coverage do
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d718a651ec3a09f5b1cc804ebbccaf4
https://doi.org/10.23919/acc.2018.8431625
https://doi.org/10.23919/acc.2018.8431625
Publikováno v:
ICRA
We explore beyond existing work in learning from demonstration by asking the question: “Can robots learn to guide?”, that is, can a robot autonomously learn an instructional policy from expert demonstration and use it to instruct humans in execut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1cc92af500e371a401be5fc8219d65cd
https://doi.org/10.1109/icra.2018.8462923
https://doi.org/10.1109/icra.2018.8462923