6 accepted papers
We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dependence on task-specific fine-tuning. DualMind uses a novel "Dual-phase" training strategy that emulates how humans lear…
Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining representations without explicit labels. When it comes to sequential decision-making tasks, however, it is difficult to prop…
Machines are a long way from robustly solving open-world perception-control tasks, such as first-person view (FPV) aerial navigation. While recent advances in end-to- end Machine Learning, especially Imitation Learning and Reinforcement appear promising, they are constrained by the need of large amo…
Reliable detection and reconstruction of wires is one of the hardest problems in the UAV community, with a wide ranging impact in the industry in terms of wire avoidance capabilities and powerline corridor inspection. In this work, we introduce a real-time, model-based, multi-view algorithm to recon…
Wire detection, depth estimation and avoidance is one of the hardest challenges towards the ubiquitous presence of robust autonomous aerial vehicles. We present an approach and a system which tackles these three challenges along with generic obstacle avoidance as well. First, we perform monocular wi…
Wire detection is a key capability for safe navigation of autonomous aerial vehicles and is a challenging problem as wires are generally only a few pixels wide, can appear at any orientation and location, and are hard to distinguish from other similar looking lines and edges. We leverage the recent…