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Found 16 papers, 7 papers with code
Practical tradeoffs between memory, compute, and performance in learned optimizers
We further leverage our analysis to construct a learned optimizer that is both faster and more memory efficient than previous work.
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs.
On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation
We identify an issue in recent approaches to learning-based control that reformulate systems with uncertain dynamics using a stochastic differential equation.
Bayesian Embeddings for Few-Shot Open World Recognition
As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information.
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles.
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component.
Particle MPC for Uncertain and Learning-Based Control
As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essential for robust performance.
Sparse Longitudinal Representations of Electronic Health Record Data for the Early Detection of Chronic Kidney Disease in Diabetic Patients
Chronic kidney disease (CKD) is a gradual loss of renal function over time, and it increases the risk of mortality, decreased quality of life, as well as serious complications.
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework
In this work, we propose a practical and theoretically-justified approach to maintaining safety in the presence of dynamics uncertainty.
Deep Reinforcement Learning amidst Lifelong Non-Stationarity
As humans, our goals and our environment are persistently changing throughout our lifetime based on our experiences, actions, and internal and external drives.
Continuous Meta-Learning without Tasks
In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
In this paper, we formulate a novel Robot Offloading Problem --- how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication?
Robust and Adaptive Planning under Model Uncertainty
The first, RAMCP-F, converges to an optimal risk-sensitive policy without having to rebuild the search tree as the underlying belief over models is perturbed.
Meta-Learning Priors for Efficient Online Bayesian Regression
However, this approach suffers from two main drawbacks: (1) it is computationally inefficient, as computation scales poorly with the number of samples; and (2) it can be data inefficient, as encoding prior knowledge that can aid the model through the choice of kernel and associated hyperparameters is often challenging and unintuitive.
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning
Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance.
Learning Sampling Distributions for Robot Motion Planning
This paper proposes a methodology for non-uniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling.
