Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation

Long-term operations of resource-constrained robots typically require hard decisions be made about which data to process and/or retain. The question then arises of how to choose which data is most useful to keep to achieve the task at hand. As spacial scale grows, the size of the map will grow without bound, and as temporal scale grows, the number of measurements will grow without bound. In this work, we present the first known approach to tackle both of these issues. The approach has two stages. First, a subset of the variables (focused variables) is selected that are most useful for a particular task. Second, a task-agnostic and principled method (focused inference) is proposed to select a subset of the measurements that maximizes the information over the focused variables. The approach is then applied to the specific task of robot navigation in an obstacle-laden environment. A landmark selection method is proposed to minimize the probability of collision and then select the set of measurements that best localizes those landmarks. It is shown that the two-stage approach outperforms both only selecting measurement and only selecting landmarks in terms of minimizing the probability of collision. The performance improvement is validated through detailed simulation and real experiments on a Pioneer robot.United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-11-1-0391)United States. Office of Naval Research (Grant N00014-11-1-0688)National Science Foundation (U.S.) (Award IIS-1318392

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ

Deep Drone Racing: From Simulation to Reality with Domain Randomization

Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network (CNN). The resulting modular system is both platform- and domain-independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854

Air Learning: An AI Research Platform for Algorithm-Hardware Benchmarking of Autonomous Aerial Robots

We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies' performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $40\%$ longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73\% to 0.5\%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute's choice affects the aerial robot's performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, \airl enables a broad class of deep RL research on UAVs. The source code is available at:~\texttt{\url{http://bit.ly/2JNAVb6}}.Comment: To Appear in Springer Machine Learning Journal (Special Issue on Reinforcement Learning for Real Life

Leveraging self-supervision for visual embodied navigation with neuralized potential fields

Une tâche fondamentale en robotique consiste à naviguer entre deux endroits. En particulier, la navigation dans le monde réel nécessite une planification à long terme à l'aide d'images RVB (RGB) en haute dimension, ce qui constitue un défi considérable pour les approches d'apprentissage de bout-en-bout. Les méthodes semi-paramétriques actuelles parviennent plutôt à atteindre des objectifs éloignés en combinant des modèles paramétriques avec une mémoire topologique de l'environnement, souvent représentée sous forme d'un graphe ayant pour nœuds des images précédemment vues. Cependant, l'utilisation de ces graphes implique généralement l'ajustement d'heuristiques d'élagage afin d'éviter les arêtes superflues, limiter la mémoire requise et permettre des recherches raisonnablement rapides dans le graphe. Dans cet ouvrage, nous montrons comment les approches de bout-en-bout basées sur l'apprentissage auto-supervisé peuvent exceller dans des tâches de navigation à long terme. Nous présentons initialement Duckie-Former (DF), une approche de bout-en-bout pour la navigation visuelle dans des environnements routiers. En utilisant un Vision Transformer (ViT) pré-entraîné avec une méthode auto-supervisée, nous nous inspirons des champs de potentiels afin de dériver une stratégie de navigation utilisant en entrée un masque de segmentation d'image de faible résolution. DF est évalué dans des tâches de navigation de suivi de voie et d'évitement d'obstacles. Nous présentons ensuite notre deuxième approche intitulée One-4-All (O4A). O4A utilise l'apprentissage auto-supervisé et l'apprentissage de variétés afin de créer un pipeline de navigation de bout-en-bout sans graphe permettant de spécifier l'objectif à l'aide d'une image. La navigation est réalisée en minimisant de manière vorace une fonction de potentiel définie de manière continue dans l'espace latent O4A. Les deux systèmes sont entraînés sans interagir avec le simulateur ou le robot sur des séquences d'exploration de données RVB et de contrôles non experts. Ils ne nécessitent aucune mesure de profondeur ou de pose. L'évaluation est effectuée dans des environnements simulés et réels en utilisant un robot à entraînement différentiel.A fundamental task in robotics is to navigate between two locations. Particularly, real-world navigation can require long-horizon planning using high-dimensional RGB images, which poses a substantial challenge for end-to-end learning-based approaches. Current semi-parametric methods instead achieve long-horizon navigation by combining learned modules with a topological memory of the environment, often represented as a graph over previously collected images. However, using these graphs in practice typically involves tuning various pruning heuristics to prevent spurious edges, limit runtime memory usage, and allow reasonably fast graph queries. In this work, we show how end-to-end approaches trained through Self-Supervised Learning (SSL) can excel in long-horizon navigation tasks. We initially present Duckie-Former (DF), an end-to-end approach for visual servoing in road-like environments. Using a Vision Transformer (ViT) pretrained with a self-supervised method, we derive a potential-fields-like navigation strategy based on a coarse image segmentation model. DF is assessed in the navigation tasks of lane-following and obstacle avoidance. Subsequently, we introduce our second approach called One-4-All (O4A). O4A leverages SSL and manifold learning to create a graph-free, end-to-end navigation pipeline whose goal is specified as an image. Navigation is achieved by greedily minimizing a potential function defined continuously over the O4A latent space. O4A is evaluated in complex indoor environments. Both systems are trained offline on non-expert exploration sequences of RGB data and controls, and do not require any depth or pose measurements. Assessment is performed in simulated and real-world environments using a differential-drive robot