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Lingyun Chen

20 accepted papers

2026

Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning

Allen Emmanuel Binny, Mahathi Anand, Hugo T. M. Kussaba, Lingyun Chen, Shreenabh Agrawal, Fares J. Abu-Dakka +1
RA-L 2026

Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S<inline-formula xmlns:mml="http://www.w3.org/1998/Math/Mat

Cited by 1Source ↗code ↗Scholar ↗
2026

Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning

Allen Emmanuel Binny, Mahathi Anand, Hugo Tadashi Kussaba, Lingyun Chen, Shreenabh Agrawal, Fares Abu-Dakka +1
ICRA 2026poster

Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S²-NNDS, a learning-from-demonstration framework that simul…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces

Jie Hu, Lingyun Chen, Geeho Kim, Jinyoung Choi, Bohyung Han, Do-Young Eun
ICML 2026spotlight

History-dependent sampling can reduce long-run Monte Carlo variance by discouraging redundant revisits, but existing schemes typically encode history through empirical measure on finite state spaces, which is infeasible in high-dimensional discrete configuration spaces or ill-posed in continuous dom…

Cited by 0Source ↗Scholar ↗
2026

TacUMI: A Multi-Modal Universal Manipulation Interface for Contact-Rich Tasks

Tailai Cheng, Kejia Chen, Lingyun Chen, Liding Zhang, Yue Zhang, Yao Ling +5
ICRA 2026poster

Task decomposition is critical for understanding and learning complex long-horizon manipulation tasks. Especially for tasks involving rich physical interactions, relying solely on visual observations and robot proprioceptive information often fails to reveal the underlying event transitions. This ra…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation

Lingyun Chen, Xinrui Zhao, Marcos P. S. Campanha, Alexander Wegener, Abdeldjallil Naceri, Abdalla Swikir +1
IROS 2025

This paper presents a novel approach for robot navigation in environments containing deformable obstacles. By integrating Learning from Demonstration (LfD) with Dynamical Systems (DS), we enable adaptive and efficient navigation in complex environments where obstacles consist of both soft and hard r

Cited by 0Source ↗code ↗Scholar ↗
2025

LEMMo-Plan: LLM-Enhanced Learning from Multi-Modal Demonstration for Planning Sequential Contact-Rich Manipulation Tasks

Kejia Chen, Zheng Shen, Yue Zhang, Lingyun Chen, Fan Wu, Zhenshan Bing +2
ICRA 2025

Large Language Models (LLMs) have gained popularity in task planning for long-horizon manipulation tasks. To enhance the validity of LLM-generated plans, visual demonstrations and online videos have been widely employed to guide the planning process. However, for manipulation tasks involving subtle

Cited by 2Source ↗code ↗Scholar ↗
2025

Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems

Shreenabh Agrawal, Hugo T. M. Kussaba, Lingyun Chen, Allen Emmanuel Binny, Pushpak Jagtap, Sami Haddadin +1
IROS 2025

Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One established technique to implement LfD in robots is to encode demonstrations in a stable Dynamical System (DS). However, f

Cited by 1Source ↗Scholar ↗
2025

TacDiffusion: Force-Domain Diffusion Policy for Precise Tactile Manipulation

Yansong Wu, Zongxie Chen, Fan Wu, Lingyun Chen, Liding Zhang, Zhenshan Bing +3
ICRA 2025

Assembly is a crucial skill for robots in both modern manufacturing and service robotics. However, mastering transferable insertion skills that can handle a variety of high-precision assembly tasks remains a significant challenge. This paper presents a novel framework that utilizes diffusion models

Cited by 41Source ↗Scholar ↗
2024

1 kHz Behavior Tree for Self-adaptable Tactile Insertion

Yansong Wu, Fan Wu, Lingyun Chen, Kejia Chen, Samuel Schneider, Lars Johannsmeier +4
ICRA 2024poster

Insertion is an essential skill for robots in both modern manufacturing and services robotics. In our previous study, we proposed an insertion skill framework based on forcedomain wiggle motion. The main limitation of this method lies in the robot’s inability to adjust its behavior according to chan…

Cited by 5Source ↗Scholar ↗
2024

Autonomous and Teleoperation Control of a Drawing Robot Avatar

Lingyun Chen, Abdeldjallil Naceri, Abdalla Swikir, Sandra Hirche, Sami Haddadin
ICRA 2024poster

A drawing robot avatar is a robotic system that allows for telepresence-based drawing, enabling users to remotely control a robotic arm and create drawings in real-time from a remote location. The proposed control framework aims to improve bimanual robot telepresence quality by reducing the user wor…

Cited by 0Source ↗Scholar ↗
2024

Demonstration to Adaptation: A User-Guided Framework for Sequential and Real-Time Planning

Kuanqi Cai, Riddhiman Laha, Yuhe Gong, Lingyun Chen, Liding Zhang, Luis F.C. Figueredo +1
IROS 2024poster

This paper introduces a comprehensive user-guided planning framework designed for robots operating in dynamic, human-centered environments – where the ability to execute sequential tasks flexibly and adaptively is paramount. Our planner enables robots to (i) encode object-centric constraints and use…

Cited by 2Source ↗Scholar ↗
2024

Elliptical K-Nearest Neighbors - Path Optimization via Coulomb’s Law and Invalid Vertices in C-space Obstacles

Liding Zhang, Zhenshan Bing, Yu Zhang, Kuanqi Cai, Lingyun Chen, Fan Wu +2
IROS 2024poster

Path planning has long been an important and active research area in robotics. To address challenges in high-dimensional motion planning, this study introduces the Force Direction Informed Trees (FDIT*), a sampling-based planner designed to enhance speed and cost-effectiveness in pathfinding. FDIT*…

Cited by 1Source ↗Scholar ↗
2024

Flexible Informed Trees (FIT*): Adaptive Batch-Size Approach in Informed Sampling-Based Path Planning

Liding Zhang, Zhenshan Bing, Kejia Chen, Lingyun Chen, Kuanqi Cai, Yu Zhang +5
IROS 2024poster

In path planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sampling-based planners. A notable example is Batch Informed Trees (BIT*), where planners iteratively determine paths to batches of vertices within the exploration area. However, utilizing a consistent…

Cited by 6Source ↗Scholar ↗
2024

Identification and validation of the dynamic model of a tendon-driven anthropomorphic finger

Junnan Li, Lingyun Chen, Johannes Ringwald, Edmundo Pozo Fortunić, Amartya Ganguly, Sami Haddadin
IROS 2024poster

This study addresses the absence of an identification framework to quantify a comprehensive dynamic model of human and anthropomorphic tendon-driven fingers, which is necessary to investigate the physiological properties of human fingers and improve the control of robotic hands. First, a generalized…

Cited by 0Source ↗Scholar ↗
2024

Learning Barrier-Certified Polynomial Dynamical Systems for Obstacle Avoidance with Robots

Martin Schonger, Hugo T. M. Kussaba, Lingyun Chen, Luis Figueredo, Abdalla Swikir, Aude Billard +1
ICRA 2024poster

Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots’ resilience to perturbations during tasks that involve static obstacle avoidance, we propose incorporating barrier certificates into an optimization pro…

Cited by 1Source ↗code ↗Scholar ↗
2024

OPENGRASP-LITE Version 1.0: A Tactile Artificial Hand with a Compliant Linkage Mechanism

Sonja Groß, Michael Ratzel, Edgar Welte, Diego Hidalgo-Carvajal, Lingyun Chen, Edmundo Pozo Fortunić +3
IROS 2024poster

Recent advancements in artificial hand development have primarily concentrated on enhancing adaptive grasping, dexterity, as well as the integration of biomimetic skin. However, few designs have successfully combined lightweight, cost-effective solutions, and tactile sensing along with adaptive gras…

Cited by 0Source ↗Scholar ↗
2024

Trajectory Planning for Non-Prehensile Object Transportation

Lingyun Chen, Haoyu Yu, Liding Zhang, Abdeldjallil Naceri, Abdalla Swikir, Sami Haddadin
IROS 2024poster

Non-prehensile transportation of unstable objects presents a challenging task in robotics. To ensure the success of the transportation, it is necessary to consider both the object’s stability via contact dynamics and the motion constraints of the robot. We propose two novel trajectory planning metho…

Cited by 0Source ↗Scholar ↗
2023

A Passivity-based Approach on Relocating High-Frequency Robot Controller to the Edge Cloud

Xiao Chen, Hamid Sadeghian, Lingyun Chen, Mario Tröbinger, Abadalla Swirkir, Abdeldjallil Naceri +1
ICRA 2023poster

As robots become more and more intelligent, the complexity of the algorithms behind them is increasing. Since these algorithms require high computation power from the onboard robot controller, the weight of the robot and energy consumption increases. A promising solution to tackle this issue is to r…

Cited by 5Source ↗Scholar ↗
2023

Towards Task-Specific Modular Gripper Fingers: Automatic Production of Fingertip Mechanics

Johannes Ringwald, Samuel Schneider, Lingyun Chen, Dennis Knobbe, Lars Johannsmeier, Abdalla Swikir +1
RA-L 2023

The adaption of robotic assembly lines to new products is generally slow and costly, binding the economical usage of a single line to a few products manufactured in masses. An important time factor is the adaption of the assembly robot's gripper fingers to the new product components - since finger d

Cited by 10Source ↗Scholar ↗