47 citations · 93 across the 15 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
A Unifying Variational Framework for Gaussian Process Motion Planning
Lucas Cosier, Rares Iordan, Sicelukwanda Zwane +5
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints,…
Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces
Ahmet Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu
Objects we interact with and manipulate often share similar parts, such as handles, that allow us to transfer our actions flexibly due to their shared functionality. This work addr…
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions
Ahmet Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu
This paper presents a novel Learning from Demonstration (LfD) method that uses neural fields to learn new skills efficiently and accurately. It achieves this by utilizing a shared…
Sliding Touch-based Exploration for Modeling Unknown Object Shape with Multi-fingered Hands
Yiting Chen, Ahmet Ercan Tekden, Marc Peter Deisenroth +1
Efficient and accurate 3D object shape reconstruction contributes significantly to the success of a robot's physical interaction with its environment. Acquiring accurate shape info…
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…
Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation
Hadi Hajieghrary, Marc Peter Deisenroth, Yasemin Bekiroglu
In this paper, we propose to use a nonlinear adaptive PID controller to regulate the joint variables of a mobile manipulator. The motion of the mobile base forces undue disturbance…
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…
Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces
Sergio Caccamo, Yasemin Bekiroglu, Carl Henrik Ek +1
In this work we study the problem of exploring surfaces and building compact 3D representations of the environment surrounding a robot through active perception. We propose an onli…
CapriDB - Capture, Print, Innovate: A Low-Cost Pipeline and Database for Reproducible Manipulation Research
Florian T. Pokorny, Yasemin Bekiroglu, Karl Pauwels +3
We present a novel approach and database which combines the inexpensive generation of 3D object models via monocular or RGB-D camera images with 3D printing and a state of the art…