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Found 8 papers, 4 papers with code
Overcoming the Pitfalls of Prediction Error in Operator Learning for Bilevel Planning
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.
Predicate Invention for Bilevel Planning
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
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
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.
Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models
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.
The Past and Present of Imitation Learning: A Citation Chain Study
Imitation Learning is a promising area of active research.
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
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
We present a comprehensive analysis of 50 interestingness measures and classify them in accordance with the two properties.
