214 citations · 415 across the 10 of their papers we have counts for
13 papers
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots
Joonho Lee, Marko Bjelonic, Alexander Reske +3
Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban enviro…
Solving Multi-Entity Robotic Problems Using Permutation Invariant Neural Networks
Tianxu An, Joonho Lee, Marko Bjelonic +2
Challenges in real-world robotic applications often stem from managing multiple, dynamically varying entities such as neighboring robots, manipulable objects, and navigation goals.…
Evaluation of Constrained Reinforcement Learning Algorithms for Legged Locomotion
Joonho Lee, Lukas Schroth, Victor Klemm +3
Shifting from traditional control strategies to Deep Reinforcement Learning (RL) for legged robots poses inherent challenges, especially when addressing real-world physical constra…
Advanced Skills by Learning Locomotion and Local Navigation End-to-End
Nikita Rudin, David Hoeller, Marko Bjelonic +1
The common approach for local navigation on challenging environments with legged robots requires path planning, path following and locomotion, which usually requires a locomotion c…
Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain
Robin Schmid, Deegan Atha, Frederik Schöller +8
Navigating off-road with a fast autonomous vehicle depends on a robust perception system that differentiates traversable from non-traversable terrain. Typically, this depends on a…
Design and Motion Planning for a Reconfigurable Robotic Base
Johannes Pankert, Giorgio Valsecchi, Davide Baret +4
A robotic platform for mobile manipulation needs to satisfy two contradicting requirements for many real-world applications: A compact base is required to navigate through cluttere…
Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning
Eric Vollenweider, Marko Bjelonic, Victor Klemm +3
In recent years, reinforcement learning (RL) has shown outstanding performance for locomotion control of highly articulated robotic systems. Such approaches typically involve tedio…
CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge
Marco Tranzatto, Frank Mascarich, Lukas Bernreiter +38
Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems as underground settings present key challenges that can render robot autonomy h…
Collision-Free MPC for Legged Robots in Static and Dynamic Scenes
Magnus Gaertner, Marko Bjelonic, Farbod Farshidian +1
We present a model predictive controller (MPC) that automatically discovers collision-free locomotion while simultaneously taking into account the system dynamics, friction constra…
Whole-Body MPC and Online Gait Sequence Generation for Wheeled-Legged Robots
Marko Bjelonic, Ruben Grandia, Oliver Harley +3
Our paper proposes a model predictive controller as a single-task formulation that simultaneously optimizes wheel and torso motions. This online joint velocity and ground reaction…
Rolling in the Deep -- Hybrid Locomotion for Wheeled-Legged Robots using Online Trajectory Optimization
Marko Bjelonic, Prajish K. Sankar, C. Dario Bellicoso +2
Wheeled-legged robots have the potential for highly agile and versatile locomotion. The combination of legs and wheels might be a solution for any real-world application requiring…
Walking Posture Adaptation for Legged Robot Navigation in Confined Spaces
Russell Buchanan, Tirthankar Bandyopadhyay, Marko Bjelonic +3
Legged robots have the ability to adapt their walking posture to navigate confined spaces due to their high degrees of freedom. However, this has not been exploited in most common…
Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots
Marko Bjelonic, C. Dario Bellicoso, Yvain de Viragh +4
We show dynamic locomotion strategies for wheeled quadrupedal robots, which combine the advantages of both walking and driving. The developed optimization framework tightly integra…