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IDA: Informed Domain Adaptive Semantic Segmentation
To improve the UDA-SS performance, we propose an Informed Domain Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance, which aims to emphasize small-region semantics during mixup.
Taxonomy of A Decision Support System for Adaptive Experimental Design in Field Robotics
Experimental design in field robotics is an adaptive human-in-the-loop decision-making process in which an experimenter learns about system performance and limitations through interactions with a robot in the form of constructed experiments.
How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
Our short-scale navigation results show that using our learned costmaps leads to overall smoother navigation, and provides the robot with a more fine-grained understanding of the robot-terrain interactions.
Enabling Intuitive Human-Robot Teaming Using Augmented Reality and Gesture Control
Human-robot teaming offers great potential because of the opportunities to combine strengths of heterogeneous agents.
An Alert-Generation Framework for Improving Resiliency in Human-Supervised, Multi-Agent Teams
Human-supervision in multi-agent teams is a critical requirement to ensure that the decision-maker's risk preferences are utilized to assign tasks to robots.
