35 accepted papers
Effective communication is critical for coordinating multi-robot teams, yet practical deployments often face severe bandwidth constraints and frequent message loss. This paper presents a communication protocol that leverages Bloom filters to enable efficient, approximate set membership queries in mu…
For Deep Reinforcement Learning (DRL) models to deliver actual utility, they must function within production environments, which often lack the extensive computational resources of training environments. This requirement for dedicated GPU resources is not economically feasible and can be especially …
State-of-the-art approaches for 6D object pose estimation assume the availability of CAD models and require the user to manually set up physically-based rendering (PBR) pipelines for synthetic training data generation. Both factors limit the application of these methods in real-world scenarios. In t…
Task and motion planning (TAMP) is a promising approach for efficient long-horizon manipulation planning, which is a prerequisite for being able to deploy manipulation systems in human-centered environments at scale. TAMP systems often rely on so-called predicates to abstractly describe the world. T
This paper presents an Active Search policy that balances between moving the camera and removing occluding objects to search for and retrieve a target object in clutter. While both types of action can reveal unobserved parts of a scene, they typically vary in execution complexity and time. Our propo…
Detecting air flows caused by phenomena such as heat convection is valuable in multiple scenarios, including leak identification and locating thermal updrafts for extending UAV flight duration. Unfortunately, the heat signature of these flows is often too subtle to be seen by a thermal camera. While…
Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels. While impressive, these methods still require a relatively large amount of supervision and segmenting an object can tak…
Interactions with articulated objects are a challenging but important task for mobile robots. To tackle this challenge, we propose a novel closed-loop control pipeline, which integrates manipulation priors from affordance estimation with sampling-based whole-body control. We introduce the concept of…
Autonomous vehicles that operate in urban environments shall comply with existing rules and reason about the interactions with other decision-making agents. In this paper, we introduce a decentralized and communication-free interaction-aware motion planner and apply it to Autonomous Surface Vessels…
Most recently proposed methods for robotic per-ception are based on deep learning, which require very large datasets to perform well. The accuracy of a learned model is mainly dependent on the data distribution it was trained on. Thus for deploying such models, it is crucial to use training data bel…
Recently, groundbreaking results have been presented on open-vocabulary semantic image segmentation. Such methods segment each pixel in an image into arbitrary categories provided at run-time in the form of text prompts, as opposed to a fixed set of classes defined at training time. In this work, we…
In this paper we provide a practical demonstration of how the modularity in a Behavior Tree (BT) decreases the effort in programming a robot task when compared to a Finite State Machine (FSM). In recent years the way to represent a task plan to control an autonomous agent has been shifting from the…
Picking a specific object from clutter is an essential component of many manipulation tasks. Partial observations often require the robot to collect additional views of the scene before attempting a grasp. This paper proposes a closed-loop next-best-view planner that drives exploration based on occl…
Autonomous navigation among people is a com-plex problem that also exhibits considerable variation depending on the type of environment and people involved. Here we consider navigation among crowds that exhibit flow-like behavior like people moving through a train station. We propose a novel pseudo-…
Autonomously navigating a robot in everyday crowded spaces requires solving complex perception and planning challenges. When using only monocular image sensor data as input, classical two-dimensional planning approaches cannot be used. While images present a significant challenge when it comes to pe…
The evaluation of robot capabilities to navigate human crowds is essential to conceive new robots intended to operate in public spaces. This paper initiates the development of a benchmark tool to evaluate such capabilities; our long term vision is to provide the community with a simulation tool that…
This manuscript addresses the active positioning service using a multi-robot system (MRS) for providing large-scale coverage and scalability in terms of MRS size. Inspired by the coverage control problems from Wireless Sensor Network (WSN) literature, we propose a gradient-based control method where
We present a novel hierarchical POMDP framework to solve an object search and delivery task where the agent is given a prior belief about the possible item locations. Solving POMDPs is computationally demanding and, as such, applications have typically been limited to small environments. The propose…
For asymptotically optimal sampling-based path planners such as RRT*, path quality improves as the number of samples added to the motion tree increases. However, each additional sample requires a nearest-neighbor search. Calculating state transition costs can be particularly difficult in cases with…
Robot navigation is a task where reinforcement learning approaches are still unable to compete with traditional path planning. State-of-the-art methods differ in small ways, and do not all provide reproducible, openly available implementations. This makes comparing methods a challenge. Recent resear…
This paper presents an online informative path planning approach for active information gathering on three-dimensional surfaces using aerial robots. Most existing works on surface inspection focus on planning a path offline that can provide full coverage of the surface, which inherently assumes the…
This paper presents a method for predicting the probability of future connectivity between mobile robots with range-limited communication. In particular, we focus on its application to active motion planning for cooperative localization (CL). The probability of connection is modeled by the distribut…
This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion o…
State-of-the-art approaches for robot navigation among humans are typically restricted to planar movement actions. This work addresses the question of whether it can be beneficial to use interaction actions, such as saying, touching, and gesturing, for the sake of allowing robots to navigate in unst…
Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust,…
Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target. In this work we present a novel approach to obtain favorable trajectories for visual-inertial syst
While optical cameras are ubiquitous in robotics, some robots can sense the world in several sections of the electromagnetic spectrum simultaneously, which can extend their capabilities in fundamental ways. For instance, many fixed-wing UAVs carry both optical and thermal imaging cameras, potentiall
General robot grasping in clutter requires the ability to synthesize grasps that work for previously unseen objects and that are also robust to physical interactions, such as collisions with other objects in the scene. In this work, we design and train a network that predicts 6 DOF grasps from 3D sc
Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots. Especially in complex dynamic environments, the coordination boost allowed by commu…
Obtaining an accurate estimate of the local wind remains a significant challenge for small unmanned aerial vehicles (UAVs). Small UAVs often operate at low altitudes near terrain, where the wind environment can be more complex than at higher altitudes. Combined with their relatively low mass, this m…
To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes. Besides building an internal representation of the observed scene geometry, the key insight toward a truly functional understan
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
Autonomous multi-robot teams can be used in complex coordinated exploration tasks to improve exploration performance in terms of both speed and effectiveness. However, use of multi-robot systems presents additional challenges. Specifically, in domains where the robots' actions are tightly coupled, c…
Multi-robot teams offer key advantages over single robots in exploration missions by increasing efficiency (explore larger areas), reducing risk (partial mission failure with robot failures), and enabling new data collection modes (multi-modal observations). However, coordinating multiple robots to…
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…