Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Matthew R. Walter"'
Autor:
Julen Urain De Jesus, Rishabh Madan, Takuma Yoneda, Stefan Bauer, Niklas Funk, Jan Peters, Charles Schaff, Ethan K. Gordon, Siddhartha S. Srinivasa, Tapomayukh Bhattacharjee, Matthew R. Walter, Felix Widmaier, Joe Watson
Publikováno v:
IEEE Robotics and Automation Letters. 7:478-485
Dexterous manipulation is a challenging and important problem in robotics. While data-driven methods are a promising approach, current benchmarks require simulation or extensive engineering support due to the sample inefficiency of popular methods. W
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2048a35e354db1ba63c26f0c408ddbcc
https://doi.org/10.1109/lra.2021.3129139
https://doi.org/10.1109/lra.2021.3129139
Autor:
Rohan Paul, Subhro Roy, Nicholas Roy, Thomas M. Howard, Jacob Arkin, Daehyung Park, Matthew R. Walter
Publikováno v:
Prof. Roy
The goal of this article is to enable robots to perform robust task execution following human instructions in partially observable environments. A robot’s ability to interpret and execute commands is fundamentally tied to its semantic world knowled
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57e6aa5c40c130d11a7e22ab0963bb63
https://doi.org/10.1177/0278364920917755
https://doi.org/10.1177/0278364920917755
Autor:
Vitor Guizilini, Igor Vasiljevic, Jiading Fang, Rare Ambru, Greg Shakhnarovich, Matthew R. Walter, Adrien Gaidon
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198236
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bf689120b0de7d246684b730f9408fb8
https://doi.org/10.1007/978-3-031-19824-3_15
https://doi.org/10.1007/978-3-031-19824-3_15
A robot's deployment environment often involves perceptual changes that differ from what it has experienced during training. Standard practices such as data augmentation attempt to bridge this gap by augmenting source images in an effort to extend th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7796d0f256d7f893ee173bb9025aea99
Autor:
Emilio Frazzoli, Gianmarco Bernasconi, Amaury Camus, Bhairav Mehta, Matthew R. Walter, Liam Paull, Andrea Censi, Jacopo Tani, Rohit Suri, Andrea F. Daniele, Aleksandar Petrov, Anthony Courchesne, Tomasz Zaluska
Publikováno v:
IROS
As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible. Compared to other sciences, there are specific challenges to benchmarking autonomy, such as the complexity of the software s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73846c72375d5314861335d6a1dafac8
http://arxiv.org/abs/2009.04362
http://arxiv.org/abs/2009.04362
Autor:
Falcon Z. Dai, Matthew R. Walter
At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of states with samples from a Markov reward process induced by following
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bbd1b3bfbfc9a3e4bf4ca7a3a1e4d52
http://arxiv.org/abs/2002.06299
http://arxiv.org/abs/2002.06299
Autor:
Matthew R. Walter, Charles Schaff
Publikováno v:
Robotics: Science and Systems
Shared autonomy provides an effective framework for human-robot collaboration that takes advantage of the complementary strengths of humans and robots to achieve common goals. Many existing approaches to shared autonomy make restrictive assumptions t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae7d077a614aa38fe7d2b8bd995741e0
Publikováno v:
Springer Proceedings in Advanced Robotics ISBN: 9783030286187
ISRR
ISRR
In order for robots to operate effectively in homes and workplaces, they must be able to manipulate the articulated objects common within environments built for and by humans. Kinematic models provide a concise representation of these objects that en
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f87beaba1a6a006d63343e4b2f4f1259
https://doi.org/10.1007/978-3-030-28619-4_30
https://doi.org/10.1007/978-3-030-28619-4_30
Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions
Publikováno v:
ICRA
The speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction. A great deal of attention has been paid to developing models and approximate inference algorithms that impr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92db00150c78ba3c2d1b7cb794027111
http://arxiv.org/abs/1903.09243
http://arxiv.org/abs/1903.09243
Autor:
A. Kirsten Bowser, Jacopo Tani, Matthew R. Walter, Breandan Considine, Andrea Censi, Ruslan Hristov, Gianmarco Bernasconi, Andrea F. Daniele, Julian Zilly, Liam Paull, Emilio Frazzoli, Claudio Ruch, Florian Golemo, Bhairav Mehta, Manfred Diaz, Jan Hakenberg, Sunil Mallya
Publikováno v:
The Springer Series on Challenges in Machine Learning
The NeurIPS '18 Competition. From Machine Learning to Intelligent Conversations
The NeurIPS '18 Competition ISBN: 9783030291341
The NeurIPS '18 Competition. From Machine Learning to Intelligent Conversations
The NeurIPS '18 Competition ISBN: 9783030291341
Despite recent breakthroughs, the ability of deep learning and reinforcement learning to outperform traditional approaches to control physically embodied robotic agents remains largely unproven. To help bridge this gap, we present the “AI Driving O
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41fc569059db2d78589eea957d5cff7b
http://arxiv.org/abs/1903.02503
http://arxiv.org/abs/1903.02503