Papers
7
Total Citations
118
H-Index
5
About
Ryan Lober is a leading researcher in humanoid robotics, with a primary focus on whole-body control and physical human-robot interaction. His work addresses the fundamental challenge of enabling highly redundant robots to execute multiple simultaneous tasks—such as locomotion, manipulation, and balancing—without conflicts or instabilities. Lober’s major contributions include pioneering variance-modulated task prioritization in whole-body control, a technique that intelligently manages task incompatibilities to produce smoother, more reliable robot behaviors. He also developed the OCRA (Optimization-Based Control for Robotics Applications) framework, a set of platform-independent libraries that simplify the implementation of hierarchical and hybrid controllers for articulated robots, notably used on the iCub humanoid. His research has been recognized with over 100 citations across his most influential papers, including his work on the EU-funded CoDyCo project, which advanced human-aware whole-body controllers for physical interaction. By combining model-based control with reinforcement learning, Lober has pushed the boundaries of what humanoid robots can achieve in dynamic, real-world environments, making his work essential reading for anyone interested in the future of autonomous, interactive robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2Whole-body hierarchical motion and force control for humanoid robots24 citations · 2015
- 3Variance modulated task prioritization in Whole-Body Control24 citations · 2015
- 4
- 5Efficient reinforcement learning for humanoid whole-body control13 citations · 2016
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- 7