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Marc Hanheide

21 accepted papers

2026

Navigating Narrow Spaces: A Comprehensive Framework for Agricultural Robots

Geesara Kulathunga, Abdurrahman Yilmaz, Zhuoling Huang, Ibrahim Hroob, Leonardo Guevara, Jaspreet Singh +2
ICRA 2026poster

Navigating within narrow spaces is a fundamental challenge in robotics, requiring precise localisation, localisation error recovery, dynamic path planning, and adaptive control for effective manoeuvring. This paper presents a modular and perception-driven navigation framework designed for constraine…

Cited by 0Source ↗Scholar ↗
2025

Navigating Narrow Spaces: A Comprehensive Framework for Agricultural Robots

Geesara Kulathunga, Abdurrahman Yilmaz, Zhuoling Huang, Ibrahim Hroob, Jaspreet Singh, Leonardo Guevara +2
RA-L 2025

Navigating within narrow spaces is a fundamental challenge in robotics, requiring precise localisation, localisation error recovery, dynamic path planning, and adaptive control for effective manoeuvring. This paper presents a modular and perception-driven navigation framework designed for constraine

Cited by 2Source ↗Scholar ↗
2024

Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization

Ibrahim Hroob, Benedikt Mersch, Cyrill Stachniss, Marc Hanheide
RA-L 2024

Mobile robots increasingly operate in real-world environments that are subject to change over time. Accurate and robust localization is, however, crucial for the effective operation of autonomous mobile systems. In this letter, we tackle the challenge of developing a generalizable learned filter for

Cited by 4Source ↗Scholar ↗
2021

Efficient and Robust Orientation Estimation of Strawberries for Fruit Picking Applications

Nikolaus Wagner, Raymond Kirk, Marc Hanheide, Grzegorz Cielniak
ICRA 2021poster

Recent developments in agriculture have high-lighted the potential of as well as the need for the use of robotics. Various processes in this field can benefit from the proper use of state of the art technology [1], in terms of efficiency as well as quality. One of these areas is the harvesting of ri…

Cited by 25Source ↗Scholar ↗
2021

Guest Editorial: Introduction to the Special Issue on Long-Term Human Motion Prediction

Luigi Palmieri, Andrey Rudenko, Jim Mainprice, Marc Hanheide, Alexandre Alahi, Achim J. Lilienthal +1
RA-L 2021

The articles in this special section focus on long term human motion prediction. This represents a key ability for advanced autonomous systems, especially if they operate in densely crowded and highly dynamic environments. In those settings understanding and anticipating human movements is fundament

Cited by 2Source ↗Scholar ↗
2021

Navigate-and-Seek: A Robotics Framework for People Localization in Agricultural Environments

Riccardo Polvara, Francesco Del Duchetto, Gerhard Neumann, Marc Hanheide
RA-L 2021

The agricultural domain offers a working environment where many human laborers are nowadays employed to maintain or harvest crops, with huge potential for productivity gains through the introduction of robotic automation. Detecting and localizing humans reliably and accurately in such an environment

Cited by 13Source ↗Scholar ↗
2020

Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning

Vaisakh Shaj, Philipp Becker, Dieter Büchler, Harit Pandya, Niels van Duijkeren, C. James Taylor +2
CoRL 2020

Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots dealing with different contact situations. Analytic models to such processes are often unavailable or inac

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2020

Context Dependant Iterative Parameter Optimisation for Robust Robot Navigation

Adam Binch, Gautham P. Das, Jaime Pulido Fentanes, Marc Hanheide
ICRA 2020poster

Progress in autonomous mobile robotics has seen significant advances in the development of many algorithms for motion control and path planning. However, robust performance from these algorithms can often only be expected if the parameters controlling them are tuned specifically for the respective r…

Cited by 22Source ↗Scholar ↗
2020

Incorporating Spatial Constraints into a Bayesian Tracking Framework for Improved Localisation in Agricultural Environments

Muhammad W. Khan, Gautham P. Das, Marc Hanheide, Grzegorz Cielniak
IROS 2020poster

Global navigation satellite system (GNSS) has been considered as a panacea for positioning and tracking since the last decade. However, it suffers from severe limitations in terms of accuracy, particularly in highly cluttered and indoor environments. Though real-time kinematics (RTK) supported GNSS…

Cited by 15Source ↗Scholar ↗
2020

Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking

Sariah Mghames, Marc Hanheide, Amir Ghalamzan E.
IROS 2020poster

Robotic technology is increasingly considered the major mean for fruit picking. However, picking fruits in a dense cluster imposes a challenging research question in terms of motion/path planning as conventional planning approaches may not find collision-free movements for the robot to reach-and-pic…

Cited by 27Source ↗Scholar ↗
2020

Next-Best-Sense: A Multi-Criteria Robotic Exploration Strategy for RFID Tags Discovery

Riccardo Polvara, Manuel Fernández-Carmona, Gerhard Neumann, Marc Hanheide
RA-L 2020

Automated exploration is one of the most relevant applications for autonomous robots. In this letter, we propose a novel online coverage algorithm called Next-Best-Sense (NBS), an extension of the Next-Best-View class of exploration algorithms which optimizes the exploration task balancing multiple

Cited by 8Source ↗code ↗Scholar ↗
2019

Grasping Unknown Objects Based on Gripper Workspace Spheres

Mohamed Sorour, Khaled Elgeneidy, Aravinda Srinivasan, Marc Hanheide, Gerhard Neumann
IROS 2019poster

In this paper, we present a novel grasp planning algorithm for unknown objects given a registered point cloud of the target from different views. The proposed methodology requires no prior knowledge of the object, nor offline learning. In our approach, the gripper kinematic model is used to generate…

Cited by 12Source ↗Scholar ↗
2018

3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data

Li Sun, Zhi Yan, Sergi Molina Mellado, Marc Hanheide, Tom Duckett
ICRA 2018poster

This paper presents a novel 3DOF pedestrian trajectory prediction approach for autonomous mobile service robots. While most previously reported methods are based on learning of 2D positions in monocular camera images, our approach uses range-finder sensors to learn and predict 3DOF pose trajectories…

Cited by 142Source ↗Scholar ↗
2018

Artificial Intelligence for Long-Term Robot Autonomy: A Survey

Lars Kunze, Nick Hawes, Tom Duckett, Marc Hanheide, Tomás Krajník
RA-L 2018

Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty, and dull tasks. However, enabling robotic systems to perform autonomousl

Cited by 191Source ↗Scholar ↗
2018

Do Not Make the Same Mistakes Again and Again: Learning Local Recovery Policies for Navigation From Human Demonstrations

Francesco Del Duchetto, Ayse Küçükyilmaz, Luca Iocchi, Marc Hanheide
RA-L 2018

In this letter, we present a human-in-the-loop learning framework for mobile robots to generate effective local policies in order to recover from navigation failures in long-term autonomy. We present an analysis of failure and recovery cases derived from long-term autonomous operation of a mobile ro

Cited by 17Source ↗Scholar ↗
2018

Getting to Know Your Robot Customers: Automated Analysis of User Identity and Demographics for Robots in the Wild

Roberto Pinillos Herrero, Jaime Pulido Fentanes, Marc Hanheide
RA-L 2018

Long-term studies with autonomous robots “in the wild” (deployed in real-world human-inhabited environments) are among the most laborious and resource-intensive endeavors in human-robot interaction. Even if a robot system itself is robust and well-working, the analysis of the vast amounts of user da

Cited by 6Source ↗Scholar ↗
2016

Persistent localization and life-long mapping in changing environments using the Frequency Map Enhancement

Tomáš Krajník, Jaime Pulido Fentanes, Marc Hanheide, Tom Duckett
IROS 2016poster

We present a lifelong mapping and localisation system for long-term autonomous operation of mobile robots in changing environments. The core of the system is a spatio-temporal occupancy grid that explicitly represents the persistence and periodicity of the individual cells and can predict the probab…

Cited by 61Source ↗Scholar ↗
2016

Towards automated system and experiment reproduction in robotics

Florian Lier, Marc Hanheide, Lorenzo Natale, Simon Schulz, Jonathan Weisz, Sven Wachsmuth +1
IROS 2016poster

Even though research on autonomous robots and human-robot interaction accomplished great progress in recent years, and reusable soft- and hardware components are available, many of the reported findings are only hardly reproducible by fellow scientists. Usually, reproducibility is impeded because re…

Cited by 22Source ↗Scholar ↗
2015

Now or later? Predicting and maximising success of navigation actions from long-term experience

Jaime Pulido Fentanes, Bruno Lacerda, Tomáš Krajník, Nick Hawes, Marc Hanheide
ICRA 2015poster

In planning for deliberation or navigation in real-world robotic systems, one of the big challenges is to cope with change. It lies in the nature of planning that it has to make assumptions about the future state of the world, and the robot's chances of successively accomplishing actions in this fut…

Cited by 99Source ↗Scholar ↗