activity
20242026
most citedFusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview

13 citations · 17 across the 10 of their papers we have counts for

collaborators

12 papers

cs.RO2026

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…

cs.RO2026

GVLA: Geometric inductive bias for Vision-Language-Action Models

Yue Peng, Yongzhe Zhao, Artur Habuda +5

Vision-language-action (VLA) models have made rapid progress in generalist robot manipulation by harnessing semantic knowledge from pretrained vision-language backbones, but their…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2025

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…

cs.RO2025

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,…