Search Results for author:
Found 9 papers, 5 papers with code
Flow-based Domain Randomization for Learning and Sequencing Robotic Skills
Domain randomization in reinforcement learning is an established technique for increasing the robustness of control policies trained in simulation.
Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness
We propose a strategy for TAMP with Uncertainty and Risk Awareness (TAMPURA) that is capable of efficiently solving long-horizon planning problems with initial-state and action outcome uncertainty, including problems that require information gathering and avoiding undesirable and irreversible outcomes.
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
In this paper, we introduce probabilistic modeling to the inverse graphics framework to quantify uncertainty and achieve robustness in 6D pose estimation tasks.
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind
To facilitate the development of new models to bridge the gap between machine and human social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv:2102. 11938) provides a suite of tasks designed to evaluate commonsense reasoning about agents' goals and actions that even young infants exhibit.
3DP3: 3D Scene Perception via Probabilistic Programming
We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images.
From proprioception to long-horizon planning in novel environments: A hierarchical RL model
For an intelligent agent to flexibly and efficiently operate in complex environments, they must be able to reason at multiple levels of temporal, spatial, and conceptual abstraction.
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables
Here we introduce query training (QT), a mechanism to learn a PGM that is optimized for the approximate inference algorithm that will be paired with it.
Learning a generative model for robot control using visual feedback
Our method can handle noise in the observed state and noise in the controllers that we interact with.
Learning higher-order sequential structure with cloned HMMs
We show that by constraining HMMs with a simple sparsity structure inspired by biology, we can make it learn variable order sequences efficiently.
