Autonomous and targeted underwater visual monitoring and exploration using Autonomous Underwater Vehicles (AUVs) can be a challenging task due to both online and offline constraints. The online constraints comprise limited onboard storage capacity and communication bandwidth to the surface, whereas the offline constraints entail the time and effort required for the selection of desired keyframes from the video data. An example use case of targeted underwater visual monitoring is finding the most interesting visual frames of fish in a long sequence of an AUV's visual experience. This challenge of targeted informative sampling is further aggravated in murky waters with poor visibility. In this paper, we present MERLION, a novel framework that provides semantically aligned and visually enhanced summaries for murky underwater marine environment monitoring and exploration. Specifically, our framework integrates (a) an image-text model for semantically aligning the visual samples to the user's needs, (b) an image enhancement model for murky water visual data and (c) an informative sampler for summarizing the monitoring experience. We validate our proposed MERLION framework on real-world data with user studies and present qualitative and quantitative results using our evaluation metric and show improved results compared to the state-of-the-art approaches. We have open-sourced the code for MERLION at the following link https://github.com/MARVL-Lab/MERLION.git.

We present a viewpoint-based non-linear Model Predictive Control (MPC) for evacuation guiding robots. Specifically, the proposed MPC algorithm enables evacuation guiding robots to track and guide cooperative human targets in emergency scenarios. Our algorithm accounts for the environment layout as well as distances between the robot and human target and distance to the goal location. A key challenge for evacuation guiding robot is the trade-off between its planned motion for leading the target toward a goal position and staying in the target’s viewpoint while maintaining line-of-sight for guiding. We illustrate the effectiveness of our proposed evacuation guiding algorithm in both simulated and real-world environments with an Unmanned Aerial Vehicle (UAV) guiding a human. Our results suggest that using the contextual information from the environment for motion planning, increases the visibility of the guiding UAV to the human while achieving faster total evacuation time.

Multi-agent pursuit-evasion tasks involving intelligent targets are notoriously challenging coordination problems. In this paper, we investigate new ways to learn such coordinated behaviors of unmanned aerial vehicles (UAVs) aimed at keeping track of multiple evasive targets. Within a Multi-Agent Reinforcement Learning (MARL) framework, we specifically propose a variant of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method. Our approach addresses multi-target pursuit-evasion scenarios within non-stationary and unknown environments with random obstacles. In addition, given the critical role played by collective exploration in terms of detecting possible targets, we implement heterogeneous roles for the pursuers for enhanced exploratory actions balanced by exploitation (i.e. tracking) of previously identified targets. Our proposed role-based MADDPG algorithm is not only able to track multiple targets, but also is able to explore for possible targets by means of the proposed Voronoi-based rewarding policy. We implemented, tested and validated our approach in a simulation environment prior to deploying a real-world multi-robot system comprising of Crazyflie drones. Our results demonstrate that a multi-agent pursuit team has the ability to learn highly efficient coordinated control policies in terms of target tracking and exploration even when confronted with multiple fast evasive targets in complex environments.

A major challenge for deep reinforcement learning (DRL) agents is to collaborate with novel partners that were not encountered by them during the training phase. This is specifically worsened by an increased variance in action responses when the DRL agents collaborate with human partners due to the lack of consistency in human behaviors. Recent work have shown that training a single agent as the best response to a diverse population of training partners significantly increases an agent's robustness to novel partners. We further enhance the population-based training approach by introducing a Hierarchical Reinforcement Learning (HRL) based method for Human-AI Collaboration. Our agent is able to learn multiple best-response policies as its low-level policy while at the same time, it learns a high-level policy that acts as a manager which allows the agent to dynamically switch between the low-level best-response policies based on its current partner. We demonstrate that our method is able to dynamically adapt to novel partners of different play styles and skill levels in the 2-player collaborative Overcooked game environment. We also conducted a human study in the same environment to test the effectiveness of our method when partnering with real human subjects. Code is available at https://gitlab.com/marvl-hipt/hipt.

Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities.

Autonomous urban driving among human-driven cars requires a holistic understanding of road rules, driver intents and driving styles. This is challenging as a short-term, single instance, driver intent of lane change may not correspond to their driving styles for a longer duration. This paper presents an interactive behavior planner which accounts for road context, short-term driver intent, and long-term driving style to infer beliefs over the latent states of surrounding vehicles. We use a specialized Partially Observable Markov Decision Process to provide risk-averse decisions. Specifically, we consider adversarial driving scenarios caused by irrational drivers to validate the robustness of our proposed interactive behavior planner in simulation as well as on a full-size self-driving car. Our experimental results show that our algorithm enables safer and more travel time-efficient autonomous driving compared to baselines even in adversarial scenarios.

Autonomous vehicles on city roads and especially in pedestrian environments require agility to navigate narrow passages and turn in tight spaces, leading to the need for a real-time, robust and adaptable controller. In this paper, we present orientation and context aware controllers for autonomous vehicles that can closely track the reference path wit alh respect to the current state of the vehicle, environmental properties, and the desired target orientation at the desired target location. Our proposed controllers are derived from the widely used pure pursuit controller. We validate our proposed controllers with respect to the baseline pure pursuit controller in simulation and on a full-size autonomous vehicle in a pedestrian environment. Our experimental results suggest significant improvements in adaptability and tracking performance compared to the pure pursuit controller.

Government data identifies driver behaviour errors as a factor in 94% of car crashes, and autonomous vehicles (AVs), which avoids risky driver behaviours completely, are expected to reduce the number of road crashes significantly. Thus, one of the central focuses of developing AVs is to ensure safety during navigation. However, in reality, AV safety has been far below its expectation, and so far, no government has allowed for complete autonomous driving without human supervision. This paper proposes a dynamic safe path planning algorithm for AVs with Gaussian process regulated risk map. By reasonably assuming that the output of the object detection and tracking module follows a multi-variate Gaussian distribution, we put forward a safe path planning paradigm with Gaussian process regulated risk map, ensuring safety with high confidence. Both simulation results and in-vehicle tests demonstrate the effectiveness of the proposed algorithm.

We present a novel autonomous driving system which uses the road contextual information and intentions of other road users for urban driving. Unlike highways, urban environments require the drivers to follow traffic signs and signals while using their best judgment for anomalous situations. In such scenarios, a self-driving car needs to understand and take into account the uncertainties in the environment to plan and decide its action accordingly. Our planner models the intentions of the surrounding vehicles leveraging a neural network, and integrates the road contextual information to reduce its environment uncertainties and also speed up the decision making process. We validate our planner in simulation and in a real urban environment. Our experimental results show that integrating intention inference and road contextual information for prediction, planning and decision making help improve safety and efficiency of our autonomous driving system.

We present a real-time vehicle detection and tracking system to accomplish the complex task of driving behavior analysis in urban environments. We propose a robust fusion system that combines a monocular camera and a 2D Lidar. This system takes advantage of three key components: robust vehicle detection using deep learning techniques, high precision range estimation from Lidar, and road context from the prior map knowledge. The camera and Lidar sensor fusion, data association and track management are all performed in the global map coordinate system by taking into account the sensors' characteristics. Lastly, behavior reasoning is performed by examining the tracked vehicle states in the lane coordinate system in which the road context is encoded. We validated our approach by tracking a leading vehicle while it performed usual urban driving behaviors such as lane keeping, stop-and-go at intersections, lane changing, overtaking and turning. The leading vehicle was tracked consistently throughout the 2.3 km route and its behavior was classified reliably.

We address the problem of rendezvous between two agents in urban street networks. Specifically, we consider the case where the agents have variable speeds and they need to schedule a rendezvous or a meeting under uncertainty in their travel times. Examples of such a scenario range from everyday life where two people would like to coordinate a meeting while going from office to home; to a futuristic case where automated taxis would like to meet each other for load balancing passengers. The scheduling for such scenarios can easily become challenging with uncertainties such as delayed departures, road blocks due to construction or traffic congestion. Any solution for such a task is required to minimize the waiting time and the planning overhead. In this paper, we propose an algorithm that optimizes the total travel time and the waiting time for two agents to complete their respective paths from start to rendezvous and from rendezvous to goal locations subject to delays along their paths. We validate our approach with a street network database which has a cost associated with every query made to the database server. Thus our algorithm intelligently optimizes for rendezvous trajectories that effectively mitigate the scourge of traffic delays, while simultaneously limiting the number of queries through careful analysis of the informative value of each potential query.

In this paper, we examine multi-target search, where one or more targets must be found by a moving robot. Given the target's initial probability distribution or the expected search region, we present an analysis of three search strategies - Global maxima search, Local maxima search, and Spiral search. We aim at minimizing the mean-time-to-find and maximizing the total probability of finding the target. This leads to two types of illustrative performance metrics: minimum time capture and guaranteed capture. We validate the search strategies with respect to these two performance metrics. In addition, we study the effect of different target distributions on the performance of the search strategies. We also consider the practical realization of the proposed algorithms for multi-target search. The search strategies are analytically evaluated, through simulations and illustrative deployments, in open-water with an Autonomous Surface Vehicle (ASV) and drifting sensor targets.

In this paper we present an algorithm for finding a distance optimal rendezvous location with respect to both initial and target locations of the mobile agents. These agents can be humans or robots, who need to meet and split while performing a collaborative task. Our aim is to embed the meeting process within a background activity such that the agents travel through the rendezvous location while taking the shortest paths to their respective target locations. We analyze this problem in a street network scenario with two agents who are given their individual scheduled routes to complete with an underlying common goal. The agents are allowed to select any combination of the waypoints along their routes as long as they travel the shortest path and pass through the same potential rendezvous location. The total number of path combinations that the agents need to evaluate for the shortest path increases rapidly with the number of waypoints along their routes. We address this computational cost by proposing a combination of Euclidean and street network distances for a trade-off between the number of queries and a distance optimal solution.

We address the problem of arranging a meeting (or rendezvous) between two or more robots in an unknown bounded topological environment, starting at unknown locations, without any communication. The goal is to rendezvous in minimum time such that the robots can share resources for performing any global task. We specifically consider a global exploration task executed by two or more robots. Each robot explores the environment simultaneously, for a specified time, then selects potential rendezvous locations, where it expects to find other robots, and visits them. We propose a ranking criterion for selecting the order in which potential rendezvous locations will be visited. This ranking criterion associates a cost for visiting a rendezvous location and gives an expected reward of finding other agents. We evaluate the time taken to rendezvous by varying a set of conditions including: world size, number of robots, starting location of each robot and the presence of sensor noise. We present simulation results to quantify the effect of the aforementioned factors on the rendezvous time.

In this paper we describe a heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect visual footage of interesting underwater regions, from multiple scales and mediums. We discuss organizational and scheduling complexities associated with multi-robot experiments in a field robotics setting. We also present results from our field trials, where we demonstrated the use of this heterogeneous robot team to achieve multi-domain monitoring of coral reefs, based on real-time interaction with a remotely-located marine biologist.

We present MARE, an autonomous airboat robot that is suitable for exploration-oriented tasks, such as inspection of coral reefs and shallow seabeds. The combination of this platform's particular mechanical properties and its powerful software framework enables it to function in a multitude of potential capacities, including autonomous surveillance, mapping, and search operations. In this paper we describe two different exploration strategies and their implementation using the MARE platform. First, we discuss the application of an efficient coverage algorithm, for the purpose of achieving systematic exploration of a known and bounded environment. Second, we present an exploration strategy driven by surprise, which steers the robot on a path that might lead to potentially surprising observations.

We present a bimodal information analysis system for automatic emotion recognition. Our approach is based on the analysis of video sequences which combines facial expressions observed visually with acoustic features to automatically recognize five universal emotion classes: anger, disgust, happiness, sadness and surprise. We address the challenges posed during the temporal analysis of the bimodal data and introduce a novel technique for combining the best features of instantaneous and temporal based visual recognition systems. We obtain robust appearance-based visual features which we classify instantaneously and aggregate it temporally to improve the recognition rates when compared to single-frame based instantaneous classification. The performance of the system is further boosted by using the complementary audio information for the bimodal emotion recognition. We combine the two modalities at both feature and score level to compare the respective joint emotion recognition rates. The emotions are instantaneously classified using a support vector machine and sequentially aggregated based on their classification probabilities. This approach is validated on a posed audio-visual database and a natural interactive database. The experiments performed on these databases provide encouraging results with the best combined recognition rate being 82%.