7 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…
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…
Benchmarking local motion planners for navigation of mobile manipulators
Sevag Tafnakaji, Hadi Hajieghrary, Quentin Teixeira +1
There are various trajectory planners for mobile manipulators. It is often challenging to compare their performance under similar circumstances due to differences in hardware, diss…
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model
Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu +4
Visual servoing enables robotic systems to perform accurate closed-loop control, which is required in many applications. However, existing methods either require precise calibratio…
Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
Cristiana de Farias, Naresh Marturi, Rustam Stolkin +1
This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement t…
Learning a generative model for robot control using visual feedback
Nishad Gothoskar, Miguel Lázaro-Gredilla, Abhishek Agarwal +2
We introduce a novel formulation for incorporating visual feedback in controlling robots. We define a generative model from actions to image observations of features on the end-eff…