911 citations · 2.9k across the 99 of their papers we have counts for
15 papers · 1 filter
Uncertainty-aware LiDAR Panoptic Segmentation
Kshitij Sirohi, Sajad Marvi, Daniel Büscher +1
Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current lear…
Visual Language Maps for Robot Navigation
Chenguang Huang, Oier Mees, Andy Zeng +1
Grounding language to the visual observations of a navigating agent can be performed using off-the-shelf visual-language models pretrained on Internet-scale data (e.g., image capti…
Grounding Language with Visual Affordances over Unstructured Data
Oier Mees, Jessica Borja-Diaz, Wolfram Burgard
Recent works have shown that Large Language Models (LLMs) can be applied to ground natural language to a wide variety of robot skills. However, in practice, learning multi-task, la…
T3VIP: Transformation-based 3D Video Prediction
Iman Nematollahi, Erick Rosete-Beas, Seyed Mahdi B. Azad +3
For autonomous skill acquisition, robots have to learn about the physical rules governing the 3D world dynamics from their own past experience to predict and reason about plausible…
Latent Plans for Task-Agnostic Offline Reinforcement Learning
Erick Rosete-Beas, Oier Mees, Gabriel Kalweit +2
Everyday tasks of long-horizon and comprising a sequence of multiple implicit subtasks still impose a major challenge in offline robot control. While a number of prior methods aime…
PADLoC: LiDAR-Based Deep Loop Closure Detection and Registration Using Panoptic Attention
José Arce, Niclas Vödisch, Daniele Cattaneo +2
A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based met…
TrackletMapper: Ground Surface Segmentation and Mapping from Traffic Participant Trajectories
Jannik Zürn, Sebastian Weber, Wolfram Burgard
Robustly classifying ground infrastructure such as roads and street crossings is an essential task for mobile robots operating alongside pedestrians. While many semantic segmentati…
RobotIO: A Python Library for Robot Manipulation Experiments
Lukas Hermann, Max Argus, Adrian Roefer +2
Setting up robot environments to quickly test newly developed algorithms is still a difficult and time consuming process. This presents a significant hurdle to researchers interest…
USegScene: Unsupervised Learning of Depth, Optical Flow and Ego-Motion with Semantic Guidance and Coupled Networks
Johan Vertens, Wolfram Burgard
In this paper we propose USegScene, a framework for semantically guided unsupervised learning of depth, optical flow and ego-motion estimation for stereo camera images using convol…
Uncertainty-aware Panoptic Segmentation
Kshitij Sirohi, Sajad Marvi, Daniel Büscher +1
Reliable scene understanding is indispensable for modern autonomous systems. Current learning-based methods typically try to maximize their performance based on segmentation metric…
What Matters in Language Conditioned Robotic Imitation Learning over Unstructured Data
Oier Mees, Lukas Hermann, Wolfram Burgard
A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural…
Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning
Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard +1
Robots operating in the open world encounter various different environments that can substantially differ from each other. This domain gap also poses a challenge for Simultaneous L…
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics
N. Passalis, S. Pedrazzi, R. Babuska +15
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their r…
Affordance Learning from Play for Sample-Efficient Policy Learning
Jessica Borja-Diaz, Oier Mees, Gabriel Kalweit +3
Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and…
Self-Supervised Moving Vehicle Detection from Audio-Visual Cues
Jannik Zürn, Wolfram Burgard
Robust detection of moving vehicles is a critical task for any autonomously operating outdoor robot or self-driving vehicle. Most modern approaches for solving this task rely on tr…