Perla Maiolino of Soft Robotics Lab will be delivering a workshop for IROS 2020 entitled "Application-Driven soft Robotic Systems: Translational Challenges" (For more information click here).
In this paper we consider long-term navigation using fixed 2D LIDARs. We consider how localization algorithms based on scan-matching - commonly used in indoor environments - are prone to failure when exposed to a challenging real-world outdoor environment. The driving motivation behind this work is to produce a simple, robust system that can be utilized repeatedly over a long period, rather than [...]
We present a novel way to learn sampling distributions for sampling-based motion planners by making use of expert data. We learn an estimate (in a non-parametric setting) of sample densities around semantic regions of interest, and incorporate these learned distributions into a sampling-based planner to produce natural plans. Our motivation is that certain aspects of [...]
Semantic Categorization of Outdoor Scenes with Uncertainty Estimates using Multi-Class Gaussian Process Classification
Abstract— This paper presents a novel semantic categorization method for 3D point cloud data using supervised, multi-class Gaussian Process (GP) classification. In contrast to other approaches, and particularly Support Vector Machines, which probably are the most used method for this task to date, GPs have the major advantage of providing informative uncertainty estimates about the [...]
Abstract—The world we live in is labeled extensively for the benefit of humans. Yet, to date, robots have made little use of human readable text as a resource. In this paper we aim to draw attention to text as a readily available source of semantic information in robotics by implementing a system which allows robots [...]