11 papers
Rapid Embodiment Adaptation for Quadrupedal Locomotion
Dichen Li, Bo Ai, Nico Bohlinger +3
Humans readily adapt their movements as their bodies change through aging, injury, or load carrying, but learning-based robot policies often break when hardware properties shift. W…
Shape Your Body: Value Gradients for Multi-Embodiment Robot Design
Nico Bohlinger, Jan Peters
We propose to turn generalist multi-embodiment value functions into reusable models for robot design. Instead of running a new reinforcement learning co-design loop for each robot,…
Active Embodiment Identification with Reinforcement Learning for Legged Robots
Nico Bohlinger, Jan Peters
We present an active embodiment identification method for legged robots that jointly learns information-seeking behavior and explicit embodiment prediction. Using a history-augment…
Evaluation of an Actuated Spine in Agile Quadruped Locomotion
Nico Bohlinger, Piotr Kicki, Davide Tateo +2
The spine plays a crucial role in the dynamic locomotion of quadrupedal animals, improving the stability, speed, and efficiency of their gait, especially for fast-paced and highly…
Bridge the Gap: Enhancing Quadruped Locomotion with Vertical Ground Perturbations
Maximilian Stasica, Arne Bick, Nico Bohlinger +5
Legged robots, particularly quadrupeds, excel at navigating rough terrains, yet their performance under vertical ground perturbations, such as those from oscillating surfaces, rema…
One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion
Nico Bohlinger, Grzegorz Czechmanowski, Maciej Krupka +4
Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped,…