34 accepted papers
We propose combining preference and rating query types into a mixed-type query selection to learn reward functions for robotic decision making to improve scientific data collection. Mixed-type query selection allows the scientist operating a robot to specify the robot’s tradeoffs and goals in terms …
Cooperative manipulation tasks impose various structure-, task-, and robot-specific constraints on mobile manip-ulators. However, current methods struggle to model and solve these myriad constraints simultaneously. We propose a twofold solution: first, we model constraints as a family of manifolds a
We present a hybrid centralized-decentralized planning algorithm for a multi-robot system consisting of a Mothership robot and multiple Passenger robots. In this system, the Passenger robots execute tasks while the Mothership provides support. This paper addresses the challenge of planning Passenger
We propose combining preference and rating query types into a mixed-type query selection to learn reward functions for robotic decision making to improve scientific data collection. Mixed-type query selection allows the scientist operating a robot to specify the robot's tradeoffs and goals in terms
Persistent operation of Autonomous Underwater Vehicles (AUVs) without manual interruption for recharging saves time and total cost for offshore monitoring and data collection applications. In order to facilitate AUVs for long mission durations without ship support, they can be equipped with docking…
Underwater vehicle manipulator systems (UVMS) are increasingly popular platforms for performing subsea operations that require precision manipulation. While there is high demand for fully autonomous or even semi-autonomous systems, most UVMS still require human support teams. Developing new hardware…
We present a navigation framework to perform autonomous underwater docking to a wave energy converter (WEC) under various ocean conditions by incorporating flow state estimation into the design of model predictive control (MPC). Existing methods lack the ability to perform dynamic rendezvous and aut…
Kinodynamic motion planning enables autonomous robots to find efficient paths while minimizing energy expenditure and avoiding hazards in the environment. However, during plan execution, the controller may deviate from the collision-free path found by the planner due to discrepancies between plannin
Robots performing mobile manipulation in unstructured environments must identify grasp affordances quickly and with robustness to perception noise. Yet in domains such as underwater manipulation, where perception noise is severe, computation is constrained, and the environment is dynamic, existing t…
We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wid…
In this work we address the problem of 3D object detection from point clouds in data-limited environments. Training with simulated data is a common approach in such scenarios; however a sim-to-real gap exists between clean and crisp simulated clouds and noisy real clouds. Previous sim-to-real approa
We present an algorithm for learning behavior trees for robotic task planning, which alleviates the need for time-intensive or infeasible manual design of control architectures. Our method involves representing the search space of behavior trees as a formal grammar and searching over this grammar by…
Soft manipulators made of deformable materials have great promise in applications that require additional flexibility and compliance; however, these characteristics also make them difficult to simulate accurately and quickly. The lack of a fast and accurate simulator prevents motion planners from ge…
We present a new algorithm for deploying passenger robots in marsupial robot systems. A marsupial robot system consists of a carrier robot (e.g., a ground vehicle), which is highly capable and has a long mission duration, and at least one passenger robot (e.g., a short-duration aerial vehicle) trans…
We address the problem of generating navigation roadmaps for uncertain and cluttered environments represented with probabilistic occupancy maps. A key challenge is to generate roadmaps that provide connectivity through tight passages and paths around uncertain obstacles. We propose the topology-info
This paper presents a framework that uses language instructions to define the constraints and objectives for robots gathering information about their environment. Designing autonomous robotic sampling missions requires deep knowledge of both autonomy systems and scientific domain expertise. Language…
This paper presents a new coordination algorithm for decentralised multi-robot information gathering. We consider planning for an online variant of the multi-agent orienteering problem with neighbourhoods. This formulation closely aligns with a number of important tasks in robotics, including inspec…
Robots often require a model of their environment to make informed decisions. In unknown environments, the ability to infer the value of a data field from a limited number of samples is essential to many robotics applications. In this work, we propose a neural network architecture to model these spa…
In this work, we present a novel approach for modeling, and classifying between, the system load states introduced when constructing staged soft arm configurations. Through a two stage approach: (1) an LSTM calibration routine is used to identify the current load state then (2) a control input gener…
Robotic exploration requires adaptively selecting navigation goals that result in the rapid discovery and mapping of an unknown world. In many real-world environments, subtle structural cues can provide insight about the unexplored world, which may be exploited by a decision maker to improve the spe…
In this paper, we present a novel algorithm for constructing a maximally informative path for a robot in an information gathering task. We use a Self-Organizing Map (SOM) framework to discover important topological features in the information function. Using these features, we identify a set of dist…
In this work we address the challenge of predicting the missing dimension (elevation angle) from 2D underwater sonar images. The high noise levels in these images, from phenomena such as non-diffuse reflections, frequently limits the usefulness of physical models. We thus propose the utilization of…
In this letter, we build on recent advances in deep learning to improve SE(3) transformations, enabling more accurate motion estimation in mobile robots. We propose using denoising autoencoders (DAEs) to address the challenges presented by modern LIDARs. Our proposed approach is comprised of two sta
In this work we develop a novel framework that enables the real-time 3D reconstruction of underwater environments using features from 2D sonar images. Due to noisy and low-resolution imagery as compared with standard cameras, automatic feature extractors for sonar images are not reliable in many sce…
In this work, we present a Stochastic Gradient Ascent (SGA) algorithm for multi-vehicle information gathering that accounts for limitations on a vehicle's control authority caused by external forces. By representing vehicle paths using a novel action space representation, rather than a state space r…
In this work, we present a novel method for constructing a topological map of biological hotspots in an aquatic environment using a Fast Marching-based Voronoi segmentation. Using this topological map, we develop a closed form solution to the scheduling problem for any single path through the graph.…
A challenging problem for autonomous exploration is estimating the utility of future samples. In this paper, we consider the problem of placing observations over an initially unknown continuous cost field to find the least-cost path from a fixed start to a fixed goal position. We propose the adaptiv
Underwater robots beneath ocean waves can benefit from feedforward control to reduce position error. This letter proposes a method using model predictive control (MPC) to predict and counteract future disturbances from an ocean wave field. The MPC state estimator employs a linear wave theory (LWT) s
In shared autonomy, a robot and human user both have some level of control in order to achieve a shared goal. Choosing the balance of control given to the user and the robot can be a challenging problem since different users have different preferences and vary in skill levels when operating a robot.…
We present a framework for planning energy-efficient trajectories through strong and uncertain disturbances, such as ocean currents and wind fields. First, we develop an iterative optimization algorithm for path planning. The algorithm samples smoothly deformed paths around the current best path est
In this paper, we present a method to improve the navigation of tethered underwater vehicles by computing optimal paths that prevent their tethers from becoming entangled in obstacles. To accomplish this, we define the Non-Entangling Travelling Salesperson Problem (NE-TSP) as an extension of the Tra…
Robots often operate in built environments containing underlying structure that can be exploited to help predict future observations. In this work, we present a deep learning based approach to predict exit locations of buildings. This technique exploits the inherent structure of buildings to create…
We present a coactive algorithm for learning a human expert's preferences in planning trajectories for information gathering in scientific autonomy domains. The algorithm learns these preferences by iteratively presenting solutions to the expert and updating an estimated utility function based on th…
In this paper we consider the problem of routing autonomously guided robots by manipulating the cost space to induce safe trajectories in the work space. Specifically, we examine the domain of UAV traffic management in urban airspaces. Each robot does not explicitly coordinate with other vehicles in…