52 accepted papers
Robots are increasingly being deployed in public spaces such as shopping malls, sidewalks, and hospitals, where safe and socially aware navigation depends on anticipating how pedestrians respond to their presence. However, existing datasets rarely capture the full spectrum of robot-induced reactions…
Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown environments is still challenging due to uncertainty in task parameters, such as the hole position and orientation, resulti
Preference-Aligned robot navigation in human environments is typically achieved through learning-based approaches, utilizing user feedback or demonstrations for personalization. However, personal preferences are subject to change and might even be context-dependent. Yet traditional reinforcement lea
Robust and accurate proprioceptive state estimation of the main body is crucial for legged robots to execute tasks in extreme environments where exteroceptive sensors, such as LiDARs and cameras, may become unreliable. In this paper, we propose DogLegs, a state estimation system for legged robots th
For scene understanding in unstructured environments, an accurate and uncertainty-aware metric-semantic mapping is required to enable informed action selection by autonomous systems. Existing mapping methods often suffer from overconfident semantic predictions, and sparse and noisy depth sensing, le
Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their complex structures, which often result in fruit occlusions. Existing view planning
In complex scenarios where typical pick-and-place techniques are insufficient, often non-prehensile manipulation can ensure that a robot is able to fulfill its task. However, non-prehensile manipulation is challenging due to its underactuated nature with hybrid-dynamics, where a robot needs to reaso
Searching for objects in cluttered environments requires selecting efficient viewpoints and manipulation actions to remove occlusions and reduce uncertainty in object locations, shapes, and categories. In this work, we address the problem of manipulation-enhanced semantic mapping, where a robot has…
Accurate inertial parameter identification is crucial for the simulation and control of robots encountering intermittent contacts with the environment. Classically, robots' inertial parameters are obtained from CAD models that are not precise (and sometimes not available, e.g., Spot from Boston Dyna
Fruit monitoring plays an important role in crop management, and rising global fruit consumption combined with labor shortages necessitates automated monitoring with robots. However, occlusions from plant foliage often hinder accurate shape and pose estimation. Therefore, we propose an active fruit
In this paper, we address the problem of behavior-based cooperative navigation of mobile robots using safe multi-agent reinforcement learning (MARL). Our work is the first to focus on cooperative navigation without individual reference targets for the robots, using a single target for the formation'
Aligning robot navigation with human preferences is essential for ensuring comfortable, and predictable robot movement in shared spaces. While preference-based learning methods, such as reinforcement learning from human feedback (RLHF), enable this alignment, the choice of the preference collection
Active object reconstruction using autonomous robots is gaining great interest. A primary goal in this task is to maximize the information of the object to be reconstructed, given limited on-board resources. Previous view planning methods exhibit inefficiency since they rely on an iterative paradigm…
In this paper, we introduce a novel approach to centroidal state estimation, which plays a crucial role in predictive model-based control strategies for dynamic legged locomotion. Our approach uses the Koopman operator theory to transform the robot’s complex nonlinear dynamics into a linear system,…
Object reconstruction is relevant for many autonomous robotic tasks that require interaction with the environment. A key challenge in such scenarios is planning view configurations to collect informative measurements for reconstructing an initially unknown object. One-shot view planning enables effi
Horticultural tasks such as pruning and selective harvesting are labor intensive and horticultural staff are hard to find. Automating these tasks is challenging due to the semi-structured greenhouse workspaces, changing environmental conditions such as lighting, dense plant growth with many occlusio…
Neural Radiance Fields (NeRFs) are gaining significant interest for online active object reconstruction due to their exceptional memory efficiency and requirement for only posed RGB inputs. Previous NeRF-based view planning methods exhibit computational inefficiency since they rely on an iterative p…
Foresighted robot navigation in dynamic indoor environments with cost-efficient hardware necessitates the use of a lightweight yet dependable controller. So inferring the scene dynamics from sensor readings without explicit object tracking is a pivotal aspect of foresighted navigation among pedestri
Monitoring the traits of plants and fruits is a fundamental task in horticulture. With accurate measurements, farmers can predict the yield of their crops and use this information for making informed management decisions, and breeders can use it for variety selection. Agricultural robotic applicatio…
Crop monitoring is crucial for maximizing agricultural productivity and efficiency. However, monitoring large and complex structures such as sweet pepper plants presents significant challenges, especially due to frequent occlusions of the fruits. Traditional next-best view planning can lead to unstr…
Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of the reward function requires detailed domain expertise and tedious fine-tuning to ensure that agents are able to learn the desired behaviour. Using a sparse reward conveniently mitigates these challe…
For the best human-robot interaction experience, the robot's navigation policy should take into account personal preferences of the user. In this paper, we present a learning framework complemented by a perception pipeline to train a depth vision-based, personalized navigation controller from user d…
Active perception for fruit mapping and harvesting is a difficult task since occlusions occur frequently and the location as well as size of fruits change over time. State-of-the-art viewpoint planning approaches utilize computationally expensive ray casting operations to find good viewpoints aiming…
State-of-the-art legged robots are either capable of measuring torque at the output of their drive systems, or have transparent drive systems which enable the computation of joint torques from motor currents. In either case, this sensor modality is seldom used in state estimation. In this paper, we…
Viewpoint planning is an important task in any application where objects or scenes need to be viewed from different angles to achieve sufficient coverage. The mapping of confined spaces such as shelves is an especially challenging task since objects occlude each other and the scene can only be obser…
Automated agricultural applications, i.e., fruit picking require spatial information about crops and, especially, their fruits. In this paper, we present a novel deep reinforcement learning (DRL) approach to determine the next best view for automatic exploration of 3D environments with a robotic arm…
Collision-free navigation while moving amongst static and dynamic obstacles with a limited sensor range is still a great challenge for modern mobile robots. Therefore, the ability to avoid collisions with obstacles in crowded, partially observable environments is one of the most important indicators…
Autonomously driving vehicles must be able to navigate in dynamic and unpredictable environments in a collision-free manner. So far, this has only been partially achieved in driverless cars and warehouse installations where marked structures such as roads, lanes, and traffic signs simplify the motio…
Pushing objects through cluttered scenes is a challenging task, especially when the objects to be pushed have initially unknown dynamics and touching other entities has to be avoided to reduce the risk of damage. In this paper, we approach this problem by applying deep reinforcement learning to gene…
Footstep planning is the dominating approach when it comes to controlling the walk of a humanoid robot, even though a footstep plan is expensive to compute. The most prominent proposals typically spend up to a few seconds of computation time and output a sequence of up to 30 steps all the way to the…
We present PATHoBot an autonomous crop surveying and intervention robot for glasshouse environments. The aim of this platform is to autonomously gather high quality data and also estimate key phenotypic parameters. To achieve this we retro-fit an off-the-shelf pipe-rail trolley with an array of mult…
The detection of planar surfaces in a point cloud is a popular technique for the extraction of drivable or walkable surfaces and for tabletop segmentation. Unfortunately, RGB-D sensors are quite noisy and provide incomplete data, which makes the extraction of surfaces more challenging. Also, it is d…
The segmentation of a point cloud into planar primitives is a popular approach to first-line scene interpretation and is particularly useful in mobile robotics for the extraction of drivable or walkable surfaces and for tabletop segmentation for manipulation purposes. Unfortunately, the planar segme…
Modern agricultural applications require knowledge about the position and size of fruits on plants. However, occlusions from leaves typically make obtaining this information difficult. We present a novel viewpoint planning approach that builds up an octree of plants with labeled regions of interest…
Annotated datasets are essential for supervised learning. However, annotating large datasets is a tedious and time-intensive task. This paper addresses active learning in the context of semantic segmentation with the goal of reducing the human labeling effort. Our application is agricultural robotic…
Foresighted, human-aware navigation is a prerequisite for service robots acting in indoor environments. In this paper, we present a novel human-aware navigation approach that relies on long-term prediction of human movements. In particular, we consider the problem of finding a path from the robot's…
Geometric primitives are a compact and versatile representation of the environment and the objects within. From a motion planning perspective, the geometric structure can be leveraged in order to implement potentially faster and smoother motion control algorithms than it has been possible with grid-…
Perception of the local environment is a precondition for mobile robots to navigate safely in dynamic environments. Most robots, i.e., humanoids and smaller wheeled robots rely on planar regions. For humanoids, a simple 2D occupancy map as environment representation on which a path is planned is her…
Foresighted navigation is an essential skill for robots to rise from rigid factory floor installations to much more versatile mobile robots that partake in our everyday environment. The current state of the art that provides this mobility to some extent is the Dynamic Window Approach combined with a…
In this paper, we present a visual simultaneous localization and mapping (SLAM) system for humanoid robots. We introduce a new binary descriptor called DLab that exploits the combined information of color, depth, and intensity to achieve robustness with respect to uniqueness, reproducibility, and st…
Next-best-view algorithms are commonly used for covering known scenes, for example in search, maintenance, and mapping tasks. In this paper, we consider the problem of planning a strategy for covering articulated environments where the robot also has to manipulate objects to inspect obstructed areas…
With the advent of polygonal maps finding their way into the navigational software of mobile robots, the Visibility Graph can be used to search for the shortest collision-free path. The nature of the Visibility Graph-based shortest path algorithms is such that first the entire graph is computed in a…
In many situations, users walk on typical paths between specific destinations at which the service of a mobile robot is needed. Depending on the environment and the paths, step-by-step following of the human might not be the optimal solution since better paths for the robot exist. We propose to perf…
We present a simple and efficient mathematical framework suitable for generating motion in the context of a variety of robotic motion tasks ranging from low-level motor control up to high-level locomotion planning. Our concept is based on a one-dimensional second-order model that allows analytic com…
Mobile manipulators installed in warehouses and factories for conveying goods between working stations need to meet the requirements of time-critical workflows. Moreover, the systems are expected to deal with changing tasks, cluttered environments and constraints imposed by the goods to be delivered…
In this paper, we introduce an approach to efficient robot navigation through cluttered indoor environments. We propose to estimate local obstacle densities based on already detected objects and use them to predict traversal costs corresponding to potential obstacles in regions not yet observable by…
We present an approach to learn optimal navigation actions for assistance tasks in which the robot aims at efficiently reaching the final navigation goal of a human where service has to be provided. Always following the human at a close distance might hereby result in inefficient trajectories, since…
To this date, many footstep planning systems rely on external sensors for mapping and traversability analysis or on computationally expensive algorithms that do not allow for real-time calculations. In this paper, we present an approach that analyzes the environment in the vicinity of the robot with
The ability to autonomously learn a model of an environment is an important capability of a mobile robot. In this paper, we investigate the problem of exploring a scene given background information in form of a topo-metric graph of the environment. Our method is relevant for several real-world appli
Although the neurological impairments of Parkinson's disease (PD) patients are well known to go along with motor control deficits, e.g., tremor, rigidity, and reduced movement, not much is known about the motor control parameters affected by the disease. In this paper, we therefore present a novel a…
In grasping tasks carried out with humanoids, knowledge about the robot's reachable workspace is important. Without this knowledge, it might be necessary to repeatedly adapt the stance location and call an inverse kinematics solver before a valid robot configuration to reach a given grasping pose ca…
In this paper, we present a novel approach to accurately calibrate the kinematic model of a humanoid based on observations of its monocular camera. Our technique estimates the parameters of the complete model, consisting of the joint angle offsets of the whole body including the legs, as well as the…