13 citations · 17 across the 10 of their papers we have counts for
13 papers
Robot Learning on Discrete Surfaces: Theory and Applications
Matteo Dalle Vedove, Fares J. Abu-Dakka, Luigi Palopoli +2
All the objects composing our world are enclosed within surfaces. Yet, most robot learning and motion generation frameworks treat surfaces as constraints ignoring their intrinsic g…
CSymPlan: Certified Symbolic Planning and Control for High-DOF Manipulators
Aditya Narendra, Ashok Kumar Saini, Mahathi Anand +3
Robot manipulators are commonly engineered around a decoupled motion-generation stack: a planner computes a collision-free path and a lower-level controller tracks the resulting re…
GVLA: Geometric Inductive Bias for Vision-Language-Action Models
Yue Peng, Yongzhe Zhao, Artur Habuda +5
Vision-language-action (VLA) models have rapidly advanced generalist robot manipulation by harnessing the semantic knowledge of pretrained vision-language backbones. However, their…
Receding-Horizon Nullspace Optimization for Actuation-Aware Control Allocation in Omnidirectional UAVs
Riccardo Pretto, Mahmoud Hamandi, Abdullah Mohamed Ali +3
Fully actuated omnidirectional UAVs enable independent control of forces and torques along all six degrees of freedom, broadening the operational envelope for agile flight and aeri…
Learning-Based Robust Control: Unifying Exploration and Distributional Robustness for Reliable Robotics via Free Energy
Hozefa Jesawada, Giovanni Russo, Abdalla Swikir +1
A key challenge towards reliable robotic control is devising computational models that can both learn policies and guarantee robustness when deployed in the field. Inspired by the…
K-VARK: Kernelized Variance-Aware Residual Kalman Filter for Sensorless Force Estimation in Collaborative Robots
Oğuzhan Akbıyık, Naseem Alhousani, Fares J. Abu-Dakka
Reliable estimation of contact forces is crucial for ensuring safe and precise interaction of robots with unstructured environments. However, accurate sensorless force estimation r…
Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning
Allen Emmanuel Binny, Mahathi Anand, Hugo T. M. Kussaba +4
Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper,…
Stable Robot Motions on Manifolds: Learning Lyapunov-Constrained Neural Manifold ODEs
David Boetius, Abdelrahman Abdelnaby, Ashok Kumar +3
Learning stable dynamical systems from data is crucial for safe and reliable robot motion planning and control. However, extending stability guarantees to trajectories defined on R…
MV-UMI: A Scalable Multi-View Interface for Cross-Embodiment Learning
Omar Rayyan, John Abanes, Mahmoud Hafez +2
Recent advances in imitation learning have shown great promise for developing robust robot manipulation policies from demonstrations. However, this promise is contingent on the ava…
MeshDMP: Motion Planning on Discrete Manifolds using Dynamic Movement Primitives
Matteo Dalle Vedove, Fares J. Abu-Dakka, Luigi Palopoli +2
An open problem in industrial automation is to reliably perform tasks requiring in-contact movements with complex workpieces, as current solutions lack the ability to seamlessly ad…
Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements
Andrej Kruzliak, Jiri Hartvich, Shubhan P. Patni +6
This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a…
Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview
Yingbai Hu, Fares J. Abu-Dakka, Fei Chen +4
Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitatin…
Safe Execution of Learned Orientation Skills with Conic Control Barrier Functions
Zheng Shen, Matteo Saveriano, Fares J. Abu-Dakka +1
In the field of Learning from Demonstration (LfD), Dynamical Systems (DSs) have gained significant attention due to their ability to generate real-time motions and reach predefined…