Search Results for author:
Found 28 papers, 16 papers with code
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
We apply the resulting learned optimizer to a variety of neural network training tasks, where it outperforms the current state of the art learned optimizer -- at matched optimizer computational overhead -- with regard to optimization performance and meta-training speed, and is capable of generalization to tasks far different from those it was meta-trained on.
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
Learning-based behavior prediction methods are increasingly being deployed in real-world autonomous systems, e. g., in fleets of self-driving vehicles, which are beginning to commercially operate in major cities across the world.
VeLO: Training Versatile Learned Optimizers by Scaling Up
While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers.
Adaptive Robust Model Predictive Control via Uncertainty Cancellation
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.
General-Purpose In-Context Learning by Meta-Learning Transformers
We further show that the capabilities of meta-trained algorithms are bottlenecked by the accessible state size (memory) determining the next prediction, unlike standard models which are thought to be bottlenecked by parameter count.
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system.
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies
Unrolled computation graphs are prevalent throughout machine learning but present challenges to automatic differentiation (AD) gradient estimation methods when their loss functions exhibit extreme local sensitivtiy, discontinuity, or blackbox characteristics.
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems.
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST$^{EM}$, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times.
Universal Neural Functionals
A challenging problem in many modern machine learning tasks is to process weight-space features, i. e., to transform or extract information from the weights and gradients of a neural network.
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts
We study the robustness of deep reinforcement learning algorithms against distribution shifts within contextual multi-stage stochastic combinatorial optimization problems from the operations research domain.
Variational Bayesian Last Layers
We introduce a deterministic variational formulation for training Bayesian last layer neural networks.
