Search Results for author: Nishanth Kumar

Found 8 papers, 4 papers with code

Overcoming the Pitfalls of Prediction Error in Operator Learning for Bilevel Planning

1 code implementation16 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.

Decision Making Operator learning

Predicate Invention for Bilevel Planning

1 code implementation17 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.

PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX

2 code implementations8 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.

Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes

no code implementations8 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.

Active Learning

Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models

no code implementations17 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.

Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control

2 code implementations23 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.

Rate of Change Analysis for Interestingness Measures

no code implementations14 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.

General Classification

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