5 citations · 9 across the 8 of their papers we have counts for
8 papers
MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds
William Hill, Ireton Liu, Anita De Mello Koch +4
We propose a new benchmark for planning tasks based on the Minecraft game. Our benchmark contains 45 tasks overall, but also provides support for creating both propositional and nu…
Learning Efficient Abstract Planning Models that Choose What to Predict
Nishanth Kumar, Willie McClinton, Rohan Chitnis +3
An effective approach to solving long-horizon tasks in robotics domains with continuous state and action spaces is bilevel planning, wherein a high-level search over an abstraction…
Predicate Invention for Bilevel Planning
Tom Silver, Rohan Chitnis, Nishanth Kumar +4
Efficient planning in continuous state and action spaces is fundamentally hard, even when the transition model is deterministic and known. One way to alleviate this challenge is to…
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
Guangyao Zhou, Antoine Dedieu, Nishanth Kumar +4
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 scalab…
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
Sean Segal, Nishanth Kumar, Sergio Casas +4
Self-driving vehicles must perceive and predict the future positions of nearby actors in order to avoid collisions and drive safely. A learned deep learning module is often respons…
Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models
Michael Fishman, Nishanth Kumar, Cameron Allen +4
A general-purpose planning agent requires an open-scope world model: one rich enough to tackle any of the wide range of tasks it may be asked to solve over its operational lifetime…