232 citations · 381 across the 25 of their papers we have counts for
28 papers
NeuSurfEmb: A Complete Pipeline for Dense Correspondence-based 6D Object Pose Estimation without CAD Models
Francesco Milano, Jen Jen Chung, Hermann Blum +2
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 f…
Comparison between Behavior Trees and Finite State Machines
Matteo Iovino, Julian Förster, Pietro Falco +3
Behavior Trees (BTs) were first conceived in the computer games industry as a tool to model agent behavior, but they received interest also in the robotics community as an alternat…
WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAV
Florian Achermann, Thomas Stastny, Bogdan Danciu +4
Real-time high-resolution wind predictions are beneficial for various applications including safe manned and unmanned aviation. Current weather models require too much compute and…
Neural Implicit Vision-Language Feature Fields
Kenneth Blomqvist, Francesco Milano, Jen Jen Chung +2
Recently, groundbreaking results have been presented on open-vocabulary semantic image segmentation. Such methods segment each pixel in an image into arbitrary categories provided…
Multi-Agent Path Integral Control for Interaction-Aware Motion Planning in Urban Canals
Lucas Streichenberg, Elia Trevisan, Jen Jen Chung +2
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 intro…
Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps
Kenneth Blomqvist, Lionel Ott, Jen Jen Chung +1
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.…
On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications
Matteo Iovino, Julian Förster, Pietro Falco +3
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 Ma…
Learning Agent-Aware Affordances for Closed-Loop Interaction with Articulated Objects
Giulio Schiavi, Paula Wulkop, Giuseppe Rizzi +3
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 integ…
Closed-Loop Next-Best-View Planning for Target-Driven Grasping
Michel Breyer, Lionel Ott, Roland Siegwart +1
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 b…
Probabilistic network topology prediction for active planning:An adaptive algorithm and application
Liang Zhang, Zexu Zhang, Roland Siegwart +1
This paper tackles the problem of active planning to achieve cooperative localization for multi-robot systems (MRS) under measurement uncertainty in GNSS-limited scenarios. Specifi…
NavDreams: Towards Camera-Only RL Navigation Among Humans
Daniel Dugas, Olov Andersson, Roland Siegwart +1
Autonomously navigating a robot in everyday crowded spaces requires solving complex perception and planning challenges. When using only monocular image sensor data as input, classi…
Descriptellation: Deep Learned Constellation Descriptors
Chunwei Xing, Xinyu Sun, Andrei Cramariuc +5
Current descriptors for global localization often struggle under vast viewpoint or appearance changes. One possible improvement is the addition of topological information on semant…
Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses
Kenneth Blomqvist, Jen Jen Chung, Lionel Ott +1
Creating computer vision datasets requires careful planning and lots of time and effort. In robotics research, we often have to use standardized objects, such as the YCB object set…
Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground Penetrating Synthetic Aperture Radar
Rik Bähnemann, Nicholas Lawrance, Lucas Streichenberg +5
Ground penetrating radar mounted on micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar image…
Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd
Fabien Grzeskowiak, David Gonon, Daniel Dugas +7
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 be…
CalQNet -- Detection of Calibration Quality for Life-Long Stereo Camera Setups
Jiapeng Zhong, Zheyu Ye, Andrei Cramariuc +4
Many mobile robotic platforms rely on an accurate knowledge of the extrinsic calibration parameters, especially systems performing visual stereo matching. Although a number of accu…
Online Informative Path Planning for Active Information Gathering of a 3D Surface
Hai Zhu, Jen Jen Chung, Nicholas R. J. Lawrance +2
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…
Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter
Michel Breyer, Jen Jen Chung, Lionel Ott +2
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 colli…
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments
Daniel Dugas, Juan Nieto, Roland Siegwart +1
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…
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
Le Chen, Yunke Ao, Florian Tschopp +5
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…
With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance
Álvaro Serra-Gómez, Bruno Brito, Hai Zhu +2
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 coo…
Go Fetch: Mobile Manipulation in Unstructured Environments
Kenneth Blomqvist, Michel Breyer, Andrei Cramariuc +7
With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved…
Accurate Mapping and Planning for Autonomous Racing
Leiv Andresen, Adrian Brandemuehl, Alex Hönger +11
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 G…
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
Julien Kindle, Fadri Furrer, Tonci Novkovic +3
Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a re…