Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Tomas Lozano-Perez"'
Publikováno v:
Scopus-Elsevier
MIT web domain
MIT web domain
We present POMCoP, a system for online planning in collaborative domains that reasons about how its actions will affect its understanding of human intentions, and demonstrate its use in building sidekicks for cooperative games. POMCoP plans in belief
Motion planning is a ubiquitous problem that is often a bottleneck in robotic applications. We demonstrate that motion planning problems such as minimum constraint removal, belief-space planning, and visibility-aware motion planning (VAMP) benefit fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::849535af3fda2fd6619bda096438de22
http://arxiv.org/abs/2206.02305
http://arxiv.org/abs/2206.02305
A longstanding objective in classical planning is to synthesize policies that generalize across multiple problems from the same domain. In this work, we study generalized policy search-based methods with a focus on the score function used to guide th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::020ab1402178793376ff324c44e59188
http://arxiv.org/abs/2204.10420
http://arxiv.org/abs/2204.10420
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Anytime motion planners are widely used in robotics. However, the relationship between their solution quality and computation time is not well understood, and thus, determining when to quit planning and start execution is unclear. In this paper, we a
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
Publikováno v:
arXiv
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a differentiable robot algorithm and an associated model; and it is tra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::216ad766c3f6950aa720b7d00fe48c47
https://hdl.handle.net/1721.1/132313
https://hdl.handle.net/1721.1/132313
Publikováno v:
Scopus-Elsevier
We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspired by human curiosity, we propose goal-literal babbling (GLIB), a sim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8a4e46c993f9d17a8b7380dd1401911
http://arxiv.org/abs/2001.08299
http://arxiv.org/abs/2001.08299
Publikováno v:
Scopus-Elsevier
Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65593e99b93f17dec40bb48119ac44b6
Publikováno v:
Scopus-Elsevier
From CNNs to attention mechanisms, encoding inductive biases into neural networks has been a fruitful source of improvement in machine learning. Adding auxiliary losses to the main objective function is a general way of encoding biases that can help
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af52613ad8885a2f6b6a0625c8a03e8a