4 papers
Compositional Motion Generation from Demonstration with Object-Centric Neural Fields
Ahmet Ercan Tekden, Yasemin Bekiroglu
Compositionality, by organizing complex behavior as combinations of simpler elements, enables robot learning that is scalable and data efficient. Leveraging this principle, we prop…
Reactive Motion Generation via Phase-varying Neural Potential Functions
Ahmet Tekden, Dimitrios Kanoulas, Aude Billard +1
Dynamical systems (DS) methods for Learning-from-Demonstration (LfD) provide stable, continuous policies from few demonstrations. First-order dynamical systems (DS) are effective f…
Implicit Articulated Robot Morphology Modeling with Configuration Space Neural Signed Distance Functions
Yiting Chen, Xiao Gao, Kunpeng Yao +3
In this paper, we introduce a novel approach to implicitly encode precise robot morphology using forward kinematics based on a configuration space signed distance function. Our pro…
Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials
Sicelukwanda Zwane, Daniel Cheney, Curtis C. Johnson +4
Soft robots offer more flexibility, compliance, and adaptability than traditional rigid robots. They are also typically lighter and cheaper to manufacture. However, their use in re…