3 accepted papers
Providing non-conservative uncertainty quantification for function estimates derived from noisy observations remains a fundamental challenge in statistical machine learning, particularly for applications in safety-critical domains. In this work, we propose novel non-asymptotic probabilistic uniform …
This paper introduces an online motion planning algorithm and a motion generation methodology for underactuated dynamic planar walking on uneven terrain. The key idea is to utilize a database of Motion Primitives and use them as training examples in a regression methodology, which is utilized when t
This paper introduces a novel way to improve the settling time of transitions between different walking controllers. This improvement is achieved by commanding a sequence of intermediate transitions to the target controller. As a result, the state of the system enters the domain of attraction of the…