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Found 17 papers, 5 papers with code
Seeing is Believing: Belief-Space Planning with Foundation Models as Uncertainty Estimators
A promising approach to address these challenges involves planning with a library of parameterized skills, where a task planner sequences these skills to achieve goals specified in structured languages, such as logical expressions over symbolic facts.
Predicate Invention from Pixels via Pretrained Vision-Language Models
Our aim is to learn to solve long-horizon decision-making problems in highly-variable, combinatorially-complex robotics domains given raw sensor input in the form of images.
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning
Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space.
Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning
We introduce a simple formulation for such learning, where the RL problem is constructed with a special ``CallPlanner'' action that terminates the bridge policy and hands control of the agent back to the planner.
Adaptive Language-Guided Abstraction from Contrastive Explanations
Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations.
Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction
Recent developments in pretrained large language models (LLMs) applied to robotics have demonstrated their capacity for sequencing a set of discrete skills to achieve open-ended goals in simple robotic tasks.
Practice Makes Perfect: Planning to Learn Skill Parameter Policies
We consider a setting where a robot is initially equipped with (1) a library of parameterized skills, (2) an AI planner for sequencing together the skills given a goal, and (3) a very general prior distribution for selecting skill parameters.
Preference-Conditioned Language-Guided Abstraction
We observe that how humans behave reveals how they see the world.
MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds
We propose a new benchmark for planning tasks based on the Minecraft game.
Learning Efficient Abstract Planning Models that Choose What to Predict
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 of an environment is used to guide low-level decision-making.
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.
