14 accepted papers
Deep neural networks (DNNs) can deteriorate in accuracy when deployment data differs from training data. While performing online training at all timesteps can improve accuracy, it is computationally expensive. We propose DecTrain, a new algorithm that decides when to train a monocular depth DNN onli
Constructing a high-fidelity representation of the 3D scene using a monocular camera can enable a wide range of applications on low-energy devices, such as micro-robots, smartphones, and AR/VR headsets. On these devices, memory is often limited in capacity and its access often dominates the consumpt
Computing consumes a significant portion of energy in many robotics applications, especially the ones involving energy-constrained robots. In addition, memory access accounts for a significant portion of the computing energy. For mapping a 3D environment, prior approaches reduce the map size while i…
Deployment of deep neural networks (DNNs) for monocular depth estimation in safety-critical scenarios on resource-constrained platforms requires well-calibrated and efficient uncertainty estimates. However, many popular uncertainty estimation techniques, including state-of-the-art ensembles and popu…
Exploration tasks are essential to many emerging robotics applications, ranging from search and rescue to space exploration. The planning problem for exploration requires determining the best locations for future measurements that will enhance the fidelity of the map, for example, by reducing its to…
Neural architecture search (NAS) typically consists of three main steps: training a super-network, training and evaluating sampled deep neural networks (DNNs), and training the discovered DNN. Most of the existing efforts speed up some steps at the cost of a significant slowdown of other steps or sa…
Exploration of unknown environments is embedded and essential in many robotics applications. Traditional algorithms, that decide where to explore by computing the expected information gain of an incomplete map from future sensor measurements, are limited to very powerful computational platforms. In…
We study a novel class of motion planning problems, inspired by emerging low-energy robotic vehicles, such as insect-size flyers, chip-size satellites, and high-endurance autonomous blimps, for which the energy consumed by computing hardware during planning a path can be as large as the energy consu…
Information-based mapping algorithms are critical to robot exploration tasks in several applications ranging from disaster response to space exploration. Unfortunately, most existing information-based mapping algorithms are plagued by the computational difficulty of evaluating the Shannon mutual inf…
Depth sensing is a critical function for robotic tasks such as localization, mapping and obstacle detection. There has been a significant and growing interest in depth estimation from a single RGB image, due to the relatively low cost and size of monocular cameras. However, state-of-the-art single-v…
Exploration problems are fundamental to robotics, arising in various domains, ranging from search and rescue to space exploration. Many effective exploration algorithms rely on the computation of mutual information between the current map and potential future measurements in order to make planning d…
This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget. While many existing algorithms simplify networks based on the number of MACs or weights, optimizing those indirect metrics may not necessarily r…
Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applicatio…
Autonomous navigation of miniaturized robots (e.g., nano/pico aerial vehicles) is currently a grand challenge for robotics research, due to the need for processing a large amount of sensor data (e.g., camera frames) with limited on-board computational resources. In this paper we focus on the design…