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Peter Lehner

13 accepted papers

2026

Calibration of Error Distributions in Robot Kinematics for Increased Precision in Manipulation Tasks

Tim Gerstewitz, Peter Lehner, Lukas Burkhard, Natalija Topalovic, Alin Albu-Schäffer, Daniel Leidner +1
RA-L 2026

The accuracy of robotic forward kinematics is commonly improved by calibration. However, most calibration methods only take deterministic errors, such as inaccurate geometry and unknown stiffnesses, into account and neglect errors with stochastic characteristics, including joint friction, gear backl

Cited by 0Source ↗Scholar ↗
2025

Towards Autonomous Data Annotation and System-Agnostic Robotic Grasping Benchmarking with 3D-Printed Fixtures

Wout Boerdijk, Maximilian Durner, Ryo Sakagami, Peter Lehner, Rudolph Triebel
ICRA 2025

The interaction of robots with their environment requires robust object-centric perception capabilities, typically achieved using learning-based methods trained on synthetic data. However, real-world deployment demands evaluating these capabilities in relevant environments, often involving extensive

Cited by 0Source ↗code ↗Scholar ↗
2024

A Tree-Based World Model for Reducing System Complexity in Autonomous Mobile Manipulation

Ryo Sakagami, Andreas Dömel, Peter Lehner, Sebastian Riedel, Sebastian G. Brunner, Alin Albu-Schäffer +1
RA-L 2024

Mobile manipulation tasks in unstructured environments remain challenging for autonomous robots. The need to employ a diverse set of software and hardware components to solve the various subtasks inevitably increases system complexity. Knowledge exchange among such diverse components renders them hi

Cited by 1Source ↗Scholar ↗
2024

Feasibility Checking and Constraint Refinement for Shared Control in Assistive Robotics

Samuel Bustamante-Gomez, Ismael Rodríguez, Gabriel Quere, Peter Lehner, Maged Iskandar, Daniel Leidner +4
RA-L 2024

Shared control enables users with motor impairments to control high-dimensional assistive robots with low-dimensional user interfaces. The challenge is to simultaneously 1) provide support for completing daily living tasks 2) enable sufficient freedom of movement to foster user empowerment 3) ensure

Cited by 4Source ↗Scholar ↗
2023

CollisionGP: Gaussian Process-Based Collision Checking for Robot Motion Planning

Javier Muñoz, Peter Lehner, Luis Moreno, Alin Albu-Schäffer, Máximo A. Roa
RA-L 2023

Collision checking is the primitive operation of motion planning that consumes most time. Machine learning algorithms have proven to accelerate collision checking. We propose CollisionGP, a Gaussian process-based algorithm for modeling a robot's configuration space and query collision checks. Collis

Cited by 13Source ↗Scholar ↗
2022

Kinematic Transfer Learning of Sampling Distributions for Manipulator Motion Planning

Peter Lehner, Máximo A. Roa, Alin Albu-Schäffer
ICRA 2022poster

Recent research has shown that guiding sampling-based planners with sampling distributions, learned from previous experiences via density estimation, can significantly decrease computation times for motion planning. We propose an algorithm that can estimate the density from the experiences of a robo…

Cited by 7Source ↗Scholar ↗
2020

The ARCHES Space-Analogue Demonstration Mission: Towards Heterogeneous Teams of Autonomous Robots for Collaborative Scientific Sampling in Planetary Exploration

Martin J. Schuster, Marcus Gerhard Müller, Sebastian G. Brunner, Hannah Lehner, Peter Lehner, Ryo Sakagami +32
RA-L 2020

Teams of mobile robots will play a crucial role in future missions to explore the surfaces of extraterrestrial bodies. Setting up infrastructure and taking scientific samples are expensive tasks when operating in distant, challenging, and unknown environments. In contrast to current single-robot spa

Cited by 92Source ↗Scholar ↗
2019

Autonomous Parallelization of Resource-Aware Robotic Task Nodes

Sebastian Georg Brunner, Andreas Dömel, Peter Lehner, Michael Beetz, Freek Stulp
RA-L 2019

Robot task programming often leads to inefficient plans, as opportunities for parallelization and precomputation are usually not exploited by the programmer. This inefficiency is often especially obvious in mobile manipulation, where path planning and pose estimation algorithms are time-consuming op

Cited by 4Source ↗Scholar ↗
2019

Visual Repetition Sampling for Robot Manipulation Planning

En Yen Puang, Peter Lehner, Zoltan-Csaba Marton, Maximilian Durner, Rudolph Triebel, Alin Albu-Schäffer
ICRA 2019poster

One of the main challenges in sampling-based motion planners is to find an efficient sampling strategy. While methods such as Rapidly-exploring Random Tree (RRT) have shown to be more reliable in complex environments than optimization-based methods, they often require longer planning times, which re…

Cited by 7Source ↗Scholar ↗
2018

Design, Execution, and Postmortem Analysis of Prolonged Autonomous Robot Operations

Sebastian G. Brunner, Peter Lehner, Martin J. Schuster, Sebastian Riedel, Rico Belder, Daniel Leidner +3
RA-L 2018

In the context of space missions and terrestrial applications, both mission goals and task implementations for autonomous robots are becoming increasingly complex. Thus, the challenge of monitoring the achievement of task objectives and checking the correctness of their implementation is becoming mo

Cited by 8Source ↗Scholar ↗
2015

Incremental, sensor-based motion generation for mobile manipulators in unknown, dynamic environments

Peter Lehner, Arne Sieverling, Oliver Brock
ICRA 2015poster

We present an incremental method for motion generation in environments with unpredictably moving and initially unknown obstacles. The key to the method is its incremental nature: it locally augments and adapts global motion plans in response to changes in the environment, even if they significantly…

Cited by 21Source ↗Scholar ↗