Where to Drop Sensors from Aerial Robots to Monitor a Surface-Level Phenomenon?

We consider the problem of routing a team of energy-constrained Unmanned Aerial Vehicles (UAVs) to drop unmovable sensors for monitoring a task area in the presence of stochastic wind disturbances. In prior work on mobile sensor routing problems, sensors and their carrier are one integrated platform, and sensors are assumed to be able to take measurements at exactly desired locations. By contrast, airdropping the sensors onto the ground can introduce stochasticity in the landing locations of the sensors. We focus on addressing this stochasticity in sensor locations from the path-planning perspective. Specifically, we formulate the problem (Multi-UAV Sensor Drop) as a variant of the Submodular Team Orienteering Problem with one additional constraint on the number of sensors on each UAV. The objective is to maximize the Mutual Information between the phenomenon at Points of Interest (PoIs) and the measurements that sensors will take at stochastic locations. We show that such an objective is computationally expensive to evaluate. To tackle this challenge, we propose a surrogate objective with a closed-form expression based on the expected mean and expected covariance of the Gaussian Process. We propose a heuristic algorithm to solve the optimization problem with the surrogate objective. The formulation and the algorithms are validated through extensive simulations.

MAP-NBV: Multi-agent Prediction-guided Next-Best-View Planning for Active 3D Object Reconstruction

We propose MAP-NBV, a prediction-guided active algorithm for 3D reconstruction with multi-agent systems. Prediction-based approaches have shown great improvement in active perception tasks by learning the cues about structures in the environment from data. But these methods primarily focus on single-agent systems. We design a next-best-view approach that utilizes geometric measures over the predictions and jointly optimizes the information gain and control effort for efficient collaborative 3D reconstruction of the object. Our method achieves 22.75% improvement over the prediction-based single-agent approach and 15.63% improvement over the non-predictive multi-agent approach. We make our code publicly available through our project website: http://raaslab.org/projects/MAPNBV/

ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation

In a typical path planning pipeline for a ground robot, we build a map (e.g., an occupancy grid) of the environment as the robot moves around. While navigating indoors, a ground robot's knowledge about the environment may be limited due to occlusions. Therefore, the map will have many as-yet-unknown regions that may need to be avoided by a conservative planner. Instead, if a robot is able to correctly predict what its surroundings and occluded regions look like, the robot may be more efficient in navigation. In this work, we focus on predicting occupancy within the reachable distance of the robot to enable faster navigation and present a self-supervised proximity occupancy map prediction method, named ProxMaP. We show that ProxMaP generalizes well across realistic and real domains, and improves the robot navigation efficiency in simulation by \textbf{$12.40\%$} against the traditional navigation method. We share our findings on our project webpage (see https://raaslab.org/projects/ProxMaP ).

Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction

Prediction-based active perception has shown the potential to improve the navigation efficiency and safety of the robot by anticipating the uncertainty in the unknown environment. The existing works for 3D shape prediction make an implicit assumption about the partial observations and therefore cannot be used for real-world planning and do not consider the control effort for next-best-view planning. We present Pred-NBV, a realistic object shape reconstruction method consisting of PoinTr-C, an enhanced 3D prediction model trained on the ShapeNet dataset, and an information and control effort-based next-best-view method to address these issues. Pred-NBV shows an improvement of 25.46% in object coverage over the traditional method in the AirSim simulator, and performs better shape completion than PoinTr, the state-of-the-art shape completion model, even on real data obtained from a Velodyne 3D LiDAR mounted on DJI M600 Pro.

RE-MOVE: An Adaptive Policy Design Approach for Dynamic Environments via Language-Based Feedback

Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may result in catastrophic failures. To address this limitation, we propose a novel approach called RE-MOVE (\textbf{RE}quest help and \textbf{MOVE} on), which uses language-based feedback to adjust trained policies to real-time changes in the environment. In this work, we enable the trained policy to decide \emph{when to ask for feedback} and \emph{how to incorporate feedback into trained policies}. RE-MOVE incorporates epistemic uncertainty to determine the optimal time to request feedback from humans and uses language-based feedback for real-time adaptation. We perform extensive synthetic and real-world evaluations to demonstrate the benefits of our proposed approach in several test-time dynamic navigation scenarios. Our approach enable robots to learn from human feedback and adapt to previously unseen adversarial situations.

Data-Driven Distributionally Robust Optimal Control with State-Dependent Noise

This paper introduces innovative data-driven techniques for estimating the noise distribution and KL divergence bound for distributionally robust optimal control (DROC). The proposed approach addresses the limitation of traditional DROC approaches that require known ambiguity sets for the noise distribution, our approach can learn these distributions and bounds in real-world scenarios where they may not be known a priori. To evaluate the effectiveness of our approach, a navigation problem involving a car-like robot under different noise distributions is used as a numerical example. The results demonstrate that DROC combined with the proposed data-driven approaches, what we call D3ROC, provide robust and efficient control policies that outperform the traditional iterative linear quadratic Gaussian (iLQG) control approach. Moreover, it shows the effectiveness of our proposed approach in handling different noise distributions. Overall, the proposed approach offers a promising solution to real-world DROC problems where the noise distribution and KL divergence bounds may not be known a priori, increasing the practicality and applicability of the DROC framework.

Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem

We study the problem of learning a function that maps context observations (input) to parameters of a submodular function (output). Our motivating case study is a specific type of vehicle routing problem, in which a team of Unmanned Ground Vehicles (UGVs) can serve as mobile charging stations to recharge a team of Unmanned Ground Vehicles (UAVs) that execute persistent monitoring tasks. {We want to learn the mapping from observations of UAV task routes and wind field to the parameters of a submodular objective function, which describes the distribution of landing positions of the UAVs .} Traditionally, such a learning problem is solved independently as a prediction phase without considering the downstream task optimization phase. However, the loss function used in prediction may be misaligned with our final goal, i.e., a good routing decision. Good performance in the isolated prediction phase does not necessarily lead to good decisions in the downstream routing task. In this paper, we propose a framework that incorporates task optimization as a differentiable layer in the prediction phase. Our framework allows end-to-end training of the prediction model without using engineered intermediate loss that is targeted only at the prediction performance. In the proposed framework, task optimization (submodular maximization) is made differentiable by introducing stochastic perturbations into deterministic algorithms (i.e., stochastic smoothing). We demonstrate the efficacy of the proposed framework using synthetic data. Experimental results of the mobile charging station routing problem show that the proposed framework can result in better routing decisions, e.g. the average number of UAVs recharged increases, compared to the prediction-optimization separate approach.

Dynamically Finding Optimal Observer States to Minimize Localization Error with Complex State-Dependent Noise

We present DyFOS, an active perception method that Dynamically Finds Optimal States to minimize localization error while avoiding obstacles and occlusions. We consider the scenario where a ground target without any exteroceptive sensors must rely on an aerial observer for pose and uncertainty estimates to localize itself along an obstacle-filled path. The observer uses a downward-facing camera to estimate the target's pose and uncertainty. However, the pose uncertainty is a function of the states of the observer, target, and surrounding environment. To find an optimal state that minimizes the target's localization uncertainty, DyFOS uses a localization error prediction pipeline in an optimization search. Given the states mentioned above, the pipeline predicts the target's localization uncertainty with the help of a trained, complex state-dependent sensor measurement model (which is a probabilistic neural network in our case). Our pipeline also predicts target occlusion and obstacle collision to remove undesirable observer states. The output of the optimization search is an optimal observer state that minimizes target localization uncertainty while avoiding occlusion and collision. We evaluate the proposed method using numerical and simulated (Gazebo) experiments. Our results show that DyFOS is almost 100x faster than yet as good as brute force. Furthermore, DyFOS yielded lower localization errors than random and heuristic searches.

Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information

Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation. Prior works on GSG-I have used deep reinforcement learning (DRL) to learn the best policy for the agent in such an environment without any need to store the huge number of state representations for GSG-I. However, the decision-making process of DRL methods is largely opaque, which results in a lack of trust in their predictions. To tackle this issue, we present an interpretable DRL method for GSG-I that generates visualization to explain the decisions taken by the DRL algorithm. We also show that this approach performs better and works well with a simpler training regimen compared to the existing method.

Approximation Algorithms for Robot Tours in Random Fields with Guaranteed Estimation Accuracy

We study the sample placement and shortest tour problem for robots tasked with mapping environmental phenomena modeled as stationary random fields. The objective is to minimize the resources used (samples or tour length) while guaranteeing estimation accuracy. We give approximation algorithms for both problems in convex environments. These improve previously known results, both in terms of theoretical guarantees and in simulations. In addition, we disprove an existing claim in the literature on a lower bound for a solution to the sample placement problem.