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Found 6 papers, 4 papers with code
Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning
AutoMPC is a Python package that automates and optimizes data-driven model predictive control.
On the Overconfidence Problem in Semantic 3D Mapping
Semantic 3D mapping, the process of fusing depth and image segmentation information between multiple views to build 3D maps annotated with object classes in real-time, is a recent topic of interest.
Optimized Coverage Planning for UV Surface Disinfection
UV radiation has been used as a disinfection strategy to deactivate a wide range of pathogens, but existing irradiation strategies do not ensure sufficient exposure of all environmental surfaces and/or require long disinfection times.
Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts
This paper proposes a discontinuity-sensitive approach to learn the solutions of parametric optimal control problems with high accuracy.
Learning the Problem-Optimum Map: Analysis and Application to Global Optimization in Robotics
This paper describes a data-driven framework for approximate global optimization in which precomputed solutions to a sample of problems are retrieved and adapted during online use to solve novel problems.
Robotics G.1.6; I.2.9
Analysis and Observations from the First Amazon Picking Challenge
This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams.
Robotics
