1 code implementation • 16 Aug 2022 • Nishanth Kumar, Willie McClinton, Rohan Chitnis, Tom Silver, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Bilevel planning, in which a high-level search over an abstraction of an environment is used to guide low-level decision-making, is an effective approach to solving long-horizon tasks in continuous state and action spaces.
1 code implementation • 17 Mar 2022 • Tom Silver, Rohan Chitnis, Nishanth Kumar, Willie McClinton, Tomas Lozano-Perez, Leslie Pack Kaelbling, Joshua Tenenbaum
Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective.
2 code implementations • 8 Feb 2022 • Guangyao Zhou, Antoine Dedieu, Nishanth Kumar, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Shrinu Kushagra, Dileep George
PGMax is an open-source Python package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalable loopy belief propagation (LBP) in JAX.
no code implementations • 8 Apr 2021 • Sean Segal, Nishanth Kumar, Sergio Casas, Wenyuan Zeng, Mengye Ren, Jingkang Wang, Raquel Urtasun
As data collection is often significantly cheaper than labeling in this domain, the decision of which subset of examples to label can have a profound impact on model performance.
no code implementations • 17 Oct 2020 • Michael Fishman, Nishanth Kumar, Cameron Allen, Natasha Danas, Michael Littman, Stefanie Tellex, George Konidaris
Unfortunately, planning to solve any specific task using an open-scope model is computationally intractable - even for state-of-the-art methods - due to the many states and actions that are necessarily present in the model but irrelevant to that problem.
no code implementations • 8 Jan 2020 • Nishanth Kumar
Imitation Learning is a promising area of active research.
2 code implementations • 23 Oct 2019 • Jonathan Chang, Nishanth Kumar, Sean Hastings, Aaron Gokaslan, Diego Romeres, Devesh Jha, Daniel Nikovski, George Konidaris, Stefanie Tellex
We demonstrate that our model trained on 33% of the possible goals is able to generalize to more than 90% of the targets in the scene for both simulation and robot experiments.
no code implementations • 14 Dec 2017 • Nandan Sudarsanam, Nishanth Kumar, Abhishek Sharma, Balaraman Ravindran
We present a comprehensive analysis of 50 interestingness measures and classify them in accordance with the two properties.