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Christian Hartl-Nesic

14 accepted papers

2026

MIND - Multi-Feature Implicit Neural Descriptors for Robotic Surface Processing of 3D Objects with Variations in Geometry

Anish Pratheepkumar, Christian Hartl-Nesic, Markus Ikeda, Fabian Widmoser, Andreas Pichler, Markus Vincze
ICRA 2026poster

The recent shift from mass production to mass personalization leads to a production environment in which workpieces have a high degree of geometric variations. The robotic process automation in such high-mix low-volume environments poses significant challenges since predetermined robot programs are …

Cited by 0Source ↗Scholar ↗
2026

SGFAM: Semantic and Geometric Features Aggregation for Dense Shape Matching in Generalizable Robotic Manipulation

Paolo Sebeto, Christian Hartl-Nesic, Jean-Baptiste Weibel, Daniel Zimmer, Andreas Holzinger, Markus Vincze
RA-L 2026

To automate processes like polishing or cleaning at scale, robots must be able to adapt learned skills to new object instances without manual reprogramming. Applications requiring tool-surface interactions face a significant challenge in transferring manipulation strategies to novel objects due to s

Cited by 0Source ↗Scholar ↗
2026

SafeFlowMPC: Predictive and Safe Trajectory Planning for Robot Manipulators with Learning-Based Policies

Thies Oelerich, Gerald Ebmer, Christian Hartl-Nesic, Andreas Kugi
ICRA 2026poster

The emerging integration of robots into everyday life brings several major challenges. Compared to classical industrial applications, more flexibility is needed in combination with real-time reactivity. Learning-based methods can train powerful policies based on demonstrated trajectories, such that …

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2026

Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing

Alexander Wachter, Alexander Willert, Marc-Philip Ecker, Christian Hartl-Nesic
ICRA 2026poster

We present a closed-loop framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback. Instead of treating tracking errors as transient disturbances, our method exploits them as infor…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

BoundPlanner: A Convex-Set-Based Approach to Bounded Manipulator Trajectory Planning

Thies Oelerich, Christian Hartl-Nesic, Florian Beck, Andreas Kugi
RA-L 2025

Online trajectory planning enables robot manipulators to react quickly to changing environments or tasks. Many robot trajectory planners exist for known environments but are often too slow for online computations. Current methods in online trajectory planning do not find suitable trajectories in cha

Cited by 4Source ↗code ↗Scholar ↗
2025

Incremental Language Understanding for Online Motion Planning of Robot Manipulators

Mitchell Abrams, Thies Oelerich, Christian Hartl-Nesic, Andreas Kugi, Matthias Scheutz
IROS 2025

Human-robot interaction requires robots to process language incrementally, adapting their actions in real-time based on evolving speech input. Existing approaches to language-guided robot motion planning typically assume fully specified instructions, resulting in inefficient stop-and-replan behavior

Cited by 1Source ↗Scholar ↗
2025

MIND - Multi-Feature Implicit Neural Descriptors for Robotic Surface Processing of 3D Objects With Variations in Geometry

Anish Pratheepkumar, Christian Hartl-Nesic, Markus Ikeda, Fabian Widmoser, Andreas Pichler, Markus Vincze
RA-L 2025

The recent shift from mass production to mass personalization leads to a production environment in which workpieces have a high degree of geometric variations. The robotic process automation in such high-mix low-volume environments poses significant challenges since predetermined robot programs are

Cited by 0Source ↗Scholar ↗
2024

Model Predictive Trajectory Optimization With Dynamically Changing Waypoints for Serial Manipulators

Florian Beck, Minh Nhat Vu, Christian Hartl-Nesic, Andreas Kugi
RA-L 2024

Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from superordinate task planning, has been challenging for online model predi

Cited by 13Source ↗Scholar ↗
2024

ProSIP: Probabilistic Surface Interaction Primitives for Learning of Robotic Cleaning of Edges

Christoph Unger, Christian Hartl-Nesic, Minh Nhat Vu, Andreas Kugi
IROS 2024poster

Learning from demonstration (LfD) has emerged as a promising approach enabling robots to acquire complex tasks directly from human demonstrations. However, tasks involving surface interactions on freeform 3D surfaces present unique challenges in modeling and execution, especially when geometric vari…

Cited by 1Source ↗Scholar ↗
2024

Time-Optimal TCP and Robot Base Placement for Pick-and-Place Tasks in Highly Constrained Environments

Alexander Wachter, Andreas Kugi, Christian Hartl-Nesic
IROS 2024poster

This work proposes a highly parallelized optimization scheme to simultaneously optimize the robot base and tool center point (TCP) placement within a robotic work cell for a sequence of pick-and-place tasks. The placement is optimized for minimum cycle time by considering the scenario holistically,…

Cited by 0Source ↗Scholar ↗
2021

Fast Swing-Up Trajectory Optimization for a Spherical Pendulum on a 7-DoF Collaborative Robot

Minh Nhat Vu, Christian Hartl-Nesic, Andreas Kugi
ICRA 2021poster

In this paper, the experimental swing-up of a spherical pendulum mounted on a collaborative robot is presented. The complete mechanical system consists of nine degrees of freedom (DoFs). The primary focus of this work is the design of a fast trajectory planning for the swing-up by systematically inc…

Cited by 11Source ↗Scholar ↗
2021

Optimal TCP and Robot Base Placement for a Set of Complex Continuous Paths

Thomas Weingartshofer, Christian Hartl-Nesic, Andreas Kugi
ICRA 2021poster

The robot base placement of an industrial robot in flexible production lines is crucial due to the limited workspace of robots, in particular for complex continuous paths that change frequently. Costly and time-consuming repositioning of the robot can be avoided by merely adapting the tool center po…

Cited by 22Source ↗Scholar ↗