40 accepted papers
End-to-end autonomous driving methods built on vision language models (VLMs) have undergone rapid development driven by their universal visual understanding and strong reasoning capabilities obtained from the large-scale pretraining. However, we find that current VLMs struggle to understand fine-gra
Open-world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects. Transferring open-vocabulary knowledge from vision-language models (VLMs) offers a promising direction but remains challenging. However, met…
Prior knowledge vastly exists in the automation industry, especially for tasks like pick-and-place, where simple programmatic demonstrations with online generation ability can be acquired easily. How to learn a policy faster with higher flexibility and generalization ability based on these demonstra
Neuromorphic Computing (NC) and Spiking Neural Networks (SNNs) in particular are often viewed as the next generation of Neural Networks (NNs). NC is a novel bio-inspired paradigm for energy efficient neural computation, often relying on SNNs in which neurons communicate via spikes in a sparse, event
Stable and robust robotic grasping is essential for current and future robot applications. In recent works, the use of large datasets and supervised learning has enhanced speed and precision in antipodal grasping. However, these methods struggle with perception and calibration errors due to large pl
Real-world autonomous driving systems must make safe decisions in the face of rare and diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world datasets like nuScenes (open-loop) or nuPlan (closed-loop). In particular nuPlan seems to be an expressive evaluation…
The expanding use of automated vision, assistance systems, and augmented reality applications in marine settings calls for reliable and accurate horizon detection and locking. Traditional methods utilizing Inertial Measurement Units (IMU) or feature-based computer vision techniques often yield incon…
Accurate calibration is crucial for using multiple cameras to triangulate the position of objects precisely. However, it is also a time-consuming process that needs to be repeated for every displacement of the cameras. The standard approach is to use a printed pattern with known geometry to estimate…
In dynamic environments, robotic manipulators and especially cobots must be able to react to changing circumstances while in motion. This substantiates the need for quick trajectory planning algorithms that are able to cope with arbitrary velocity and acceleration boundary conditions. Apart from dyn…
We develop a graph convolutional neural network for power system contingency analysis. In contrast to other methods, the proposed architecture is purely data-driven and does not require knowledge of the power grid’s underlying topology. Instead, the estimation of multiple correlation-based graphs en…
Unmanned aerial vehicles assist in maritime search and rescue missions by flying over large search areas to autonomously search for objects or people. Reliably detecting objects of interest requires fast models to employ on embedded hardware. Moreover, with increasing distance to the ground station…
As robots become increasingly prevalent amidst diverse environments, their ability to adapt to novel scenarios and objects is essential. Advances in modern object detection have also paved the way for robots to identify interaction entities within their immediate vicinity. One drawback is that the r…
This paper introduces a novel approach to video object detection detection and tracking on Unmanned Aerial Vehicles (UAVs). By incorporating metadata, the proposed approach creates a memory map of object locations in actual world coordinates, providing a more robust and interpretable representation…
Event cameras are becoming increasingly popular in robotics and computer vision due to their beneficial properties, e.g., high temporal resolution, high bandwidth, almost no motion blur, and low power consumption. However, these cameras remain expensive and scarce in the market, making them inaccess…
Spin plays a considerable role in table tennis, making a shot's trajectory harder to read and predict. However, the spin is challenging to measure because of the ball's high velocity and the magnitude of the spin values. Existing methods either require extremely high framerate cameras or are unrelia…
Naturally, finding joint trajectories for robotic manipulators involves competing optimization goals. On the one hand, the end-effector should move along a predictable and short path while on the other hand joint movement and acceleration should be kept to a minimum. Obstacles in the workspace or jo…
Reinforcement learning (RL) has achieved some impressive recent successes in various computer games and simulations. Most of these successes are based on having large numbers of episodes from which the agent can learn. In typical robotic applications, however, the number of feasible attempts is very…
Multi-object tracking is a critical component in autonomous navigation, as it provides valuable information for decision-making. Many researchers tackled the 3D multi-object tracking task by filtering out the frame-by-frame 3D detections; however, their focus was mainly on finding useful features or…
Successful and meaningful human-robot interaction requires robots to have knowledge about the interaction context - e.g., which objects should be interacted with. Unfortunately, the corpora of interactive objects is - for all practical purposes - infinite. This fact hinders the deployment of robots…
A major challenge in current optimization research for deep learning is to automatically find optimal step sizes for each update step. The optimal step size is closely related to the shape of the loss in the update step direction. However, this shape has not yet been examined in detail. This work sh…
Simultaneous Localization and Mapping (SLAM) is one of the basic problems in mobile robotics. While most approaches are based on occupancy grid maps, Normal Distributions Transforms (NDT) and mixtures like Occupancy Normal Distribution Transforms (ONDT) have been shown to represent sensor measuremen…
In table tennis, the rotation (spin) of the ball plays a crucial role. A table tennis match will feature a variety of strokes. Each generates different amounts and types of spin. To develop a robot that can compete with a human player, the robot needs to detect spin, so it can plan an appropriate re…
Detecting and localizing contacts acting on a manipulator is a relevant problem for manipulation tasks like grasping, since contact information can be helpful for recovering from collisions or for improving the grasping performance itself. In this work, we present a solution for contact point locali…
Many robotic applications, especially exploration scenarios, benefit from deploying multiple collaborating robots with the aim of parallelizing and therefore accelerating the involved task. One critical part of a multi-robot system is its communication system. Depending on the application scenario,…
Recursive state estimation is often used to estimate a probability density function of a specific state, e.g. a robot's pose, over time. Compared to Kalman filters, Sequential Monte Carlo (SMC) methods are less constrained in regard to state propagation and update model definition, which makes it ea…
This work presents a model based traversability analysis method which employs a detailed vehicle model to perform real-time path planning in complex environments. The vehicle model represents the vehicle's wheels and chassis, allowing it to accurately predict the vehicles 3D pose, detailed contact i…
A model-based Machine Learning (ML) approach is presented to detect and localize external contacts on a 6 degree of freedom (DoF) serial manipulator. This approach only requires the use of proprioceptive sensors (joint positions, velocities and one-dimensional (ID) joint torques already available in…
Efficient 2D and 3D map representations of both static and dynamic, indoor and outdoor environments are crucial for navigation of driving and flying robots. In this paper, we propose a fast and accurate approach for 2D and 3D Normal Distributions Transform (NDT) mapping based on indexed kd-trees. Si…
Fast and robust person detection is one of the most important tasks for robotic applications involving human interaction. Particularly in mobile robotics this task is still challenging. Though there are already reliable and real-time capable approaches, they are usually computationally expensive. Th…
We present LS-ELAS, a line segment extension to the ELAS algorithm, which increases the performance and robustness. LS-ELAS is a binocular dense stereo matching algorithm, which computes the disparities in constant time for most of the pixels in the image and in linear time for a small subset of the…
A fundamental task of automated guided vehicles is transporting heavy payloads. These payloads are often given in the form of large containers that are mounted onto a cart with four caster wheels. In this paper we investigate a combined detection and control architecture that allows an AGV to reliab…
A new navigation system for outdoor person following at higher speeds (maximum speed ≈2.5 m/s) is proposed. A combination of global and local path planning, and path following control, allows a robot to follow a jogger in various outdoor scenarios, including highly dynamical environments with pedest…
A new nonlinear control law for path following with skid-steered mobile robots is proposed. A terrain dependent kinematic model is utilized in path coordinates, and the kinematic parameters are experimentally evaluated. A kinematic path following control is developed using the Lyapunov approach. A s…
Fast and accurate obstacle detection is a crucial component for autonomous robot navigation. It becomes even more important for a shared control vehicle like an electric wheeled walker, since the safety of the vehicle and the user depend on the correct classification of obstacles. This work describe…
Automated guided vehicles (AGVs) are useful for a variety of transportation tasks. They usually detect obstacles on the path they are following using 2D laser scanners. If an AGV should be deployed in a shared space with people, 3D information has to be considered as well to detect unforeseen obstac…
In this work we present a novel fingerprint similarity sensor model for the purpose of localizing a mobile robot with passive ultra-high frequency (UHF) radio-frequency identification (RFID) through location fingerprinting. We firstly evaluate the performance of probabilistic similarity measures app
This paper investigates Recurrent Neural Networks (RNNs), particularly Dynamic Cortex Memories (DCMs), an extension of Long Short Term Memories (LSTMs) for classification of 14 different ground types based on vibration data. Also a simple regularization technique called Sequence Boundary Dropout (SB
This paper presents a nonlinear control approach for quadrotor Micro Aerial Vehicles (MAVs), which combines a backstepping-like regulator based on the solution of a certain class of global output regulation problems for the rigid body equations on SO(3), a robust controller for the system with bound…
Traversable region detection is important for autonomous visual navigation of mobile robots. Only short range traversable regions can be detected using traditional methods based on stereo vision because of the limited image resolution and baseline of stereo vision. In this paper, we propose a novel…
The goal of saliency detection is to highlight objects in image data that stand out relative to their surrounding. Therefore, saliency detection aims to capture regions that are perceived as important. The most recent bottom-up approaches for saliency detection measure contrast based on visual featu…