Lucas Schulze — accepted AI-conference papers

2026

Floating-Base Deep Lagrangian Networks

Lucas Schulze, Juliano Negri, Victor Barasuol, Vivian Suzano Medeiros, Marcelo Becker, Jan Peters +1

ICRA 2026poster

Grey-box methods for system identification combine deep learning with physics-informed constraints, capturing complex dependencies while improving out-of-distribution generalization. Despite the growing importance of floating-base systems such as humanoids and quadrupeds, current grey-box models ign…

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2025

Context-Aware Deep Lagrangian Networks for Model Predictive Control

Lucas Schulze, Jan Peters, Oleg Arenz

IROS 2025

Controlling a robot based on physics-consistent dynamic models, such as Deep Lagrangian Networks (DeLaN), can improve the generalizability and interpretability of the resulting behavior. However, in complex environments, the number of objects to potentially interact with is vast, and their physical

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2024

PACC: A Passive-Arm Approach for High-Payload Collaborative Carrying with Quadruped Robots Using Model Predictive Control

Giulio Turrisi, Lucas Schulze, Vivian S. Medeiros, Claudio Semini, Victor Barasuol

IROS 2024poster

In this paper, we introduce the concept of using passive arm structures with intrinsic impedance for robot-robot and human-robot collaborative carrying with quadruped robots. The concept is meant for a leader-follower task and takes a minimalist approach that focuses on exploiting the robots’ payloa…

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