Search Results for author:
Found 12 papers, 2 papers with code
DEUX: Active Exploration for Learning Unsupervised Depth Perception
Training with data collected by our approach improves depth completion by an average greater than 18% across four depth completion models compared to existing exploration methods on the MP3D test set.
Scale-Invariant Fast Functional Registration
Functional registration algorithms represent point clouds as functions (e. g. spacial occupancy field) avoiding unreliable correspondence estimation in conventional least-squares registration algorithms.
A Second-Order Reachable Sets Computation Scheme via a Cauchy-Type Variational Hamilton-Jacobi-Isaacs Equation
Motivated by the scalability limitations of Eulerian methods for variational Hamilton-Jacobi-Isaacs (HJI) formulations that provide a least restrictive controller in problems that involve state or input constraints under a worst-possible disturbance, we introduce a second-order, successive sweep algorithm for computing the zero sublevel sets of a popular reachability value functional.
Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling
One of the preeminent obstacles to scaling multi-agent reinforcement learning to large numbers of agents is assigning credit to individual agents' actions.
Data-driven Koopman Operators for Model-based Shared Control of Human-Machine Systems
Overall, we find that model-based shared control significantly improves task and control metrics when compared to a natural learning, or user only, control paradigm.
An Ergodic Measure for Active Learning From Equilibrium
We show that our method is able to maintain Lyapunov attractiveness with respect to the equilibrium task while actively generating data for learning tasks such, as Bayesian optimization, model learning, and off-policy reinforcement learning.
Active Learning
Robotics
Active Area Coverage from Equilibrium
This paper develops a method for robots to integrate stability into actively seeking out informative measurements through coverage.
Robotics
Structured Neural Network Dynamics for Model-based Control
We present a structured neural network architecture that is inspired by linear time-varying dynamical systems.
Decentralized Ergodic Control: Distribution-Driven Sensing and Exploration for Multi-Agent Systems
We present a decentralized ergodic control policy for time-varying area coverage problems for multiple agents with nonlinear dynamics.
Robotics Systems and Control
Data-Driven Measurement Models for Active Localization in Sparse Environments
Exploration with respect to the information density based on the data-driven measurement model enables localization.
Robotics
Ergodic Exploration using Binary Sensing for Non-Parametric Shape Estimation
Current methods to estimate object shape---using either vision or touch---generally depend on high-resolution sensing.
Robotics
Real-Time Area Coverage and Target Localization using Receding-Horizon Ergodic Exploration
Although a number of solutions exist for the problems of coverage, search and target localization---commonly addressed separately---whether there exists a unified strategy that addresses these objectives in a coherent manner without being application-specific remains a largely open research question.
Robotics
