Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Rohan Chitnis"'
Autor:
Clement Gehring, Masataro Asai, Rohan Chitnis, Tom Silver, Leslie Kaelbling, Shirin Sohrabi, Michael Katz
Recent advances in reinforcement learning (RL) have led to a growing interest in applying RL to classical planning domains or applying classical planning methods to some complex RL domains. However, the long-horizon goal-based problems found in class
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3aced596ec4f20d1269d75701dea6145
http://arxiv.org/abs/2109.14830
http://arxiv.org/abs/2109.14830
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level decisions and task-level plan feasibility. TAMP approaches rely on domain
In robotic domains, learning and planning are complicated by continuous state spaces, continuous action spaces, and long task horizons. In this work, we address these challenges with Neuro-Symbolic Relational Transition Models (NSRTs), a novel class
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e9f3923e843db79707554684fbb0181
http://arxiv.org/abs/2105.14074
http://arxiv.org/abs/2105.14074
Autor:
Lisa Volpatti, Alex Hanson, Jennifer Schall, Jesse Dunietz, Amanda Chen, Rohan Chitnis, Eric Alm, Alison Takemura, Diana Chien
Publikováno v:
2020 ASEE Virtual Annual Conference Content Access Proceedings.
Publikováno v:
Web of Science
Meta-planning, or learning to guide planning from experience, is a promising approach to improving the computational cost of planning. A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::979ebeb9c59a8f5b09f9a76351a5aa59
Autor:
Rachel Holladay, Beomjoon Kim, Caelan Reed Garrett, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Rohan Chitnis, Tom Silver
The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the state of the objects, is known as task and motion planning (TAMP). TAMP p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09b53ee732edec44681e3807d1e60114
Publikováno v:
ICRA
We address the problem of effectively composing skills to solve sparse-reward tasks in the real world. Given a set of parameterized skills (such as exerting a force or doing a top grasp at a location), our goal is to learn policies that invoke these
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7b7b1dfd24d1325bfc50c3204b4ab74
http://arxiv.org/abs/1909.13874
http://arxiv.org/abs/1909.13874
Publikováno v:
MIT web domain
IROS
IROS
In partially observed environments, it can be useful for a human to provide the robot with declarative information that represents probabilistic relational constraints on properties of objects in the world, augmenting the robot's sensory observations
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6614e8f867ebe230b2d296a0e859a36
https://hdl.handle.net/1721.1/137703
https://hdl.handle.net/1721.1/137703
Publikováno v:
MIT web domain
ICRA
ICRA
Multi-object manipulation problems in continuous state and action spaces can be solved by planners that search over sampled values for the continuous parameters of operators. The efficiency of these planners depends critically on the effectiveness of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::743f5f5874070774fc461b86232228d7
http://arxiv.org/abs/1809.07878
http://arxiv.org/abs/1809.07878
Publikováno v:
Scopus-Elsevier
MIT web domain
IJCAI
MIT web domain
IJCAI
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. In many applications that involve processing high-dimensional data, it is important to identify a small set of entities that account for a significant fraction of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::240d933578c18e6c5a5692aab375426a
http://arxiv.org/abs/1805.02874
http://arxiv.org/abs/1805.02874