activity
20242026
collaborators

14 papers

cs.RO2026

A Graph-Based Reinforcement Learning Approach with Frontier Potential Based Reward for Safe Cluttered Environment Exploration

Gabriele Calzolari, Vidya Sumathy, Christoforos Kanellakis +1

Autonomous exploration of cluttered environments requires efficient exploration strategies that guarantee safety against potential collisions with unknown random obstacles. This pa…

cs.LG2026

A Survey on Data-Driven Models for Soil Moisture Regression and Classification

Ilektra Tsimpidi, George Georgoulas, Vidya Sumathy +1

Soil Moisture (SM) modelling constitutes a complex spatiotemporal learning problem characterised by nonlinear environmental interactions, heterogeneous data sources, and limited gr…

cs.RO2026

Modeling and Control of a Pneumatic Morphing Soft Quadrotor based on the SOFA Framework for Dynamic Soft Robotic Simulation

F. Labra Caso, V. Sumathy, P. Ferrentino +3

This article presents a novel SOFA based finite element method for the soft body modeling and the corresponding dynamic simulation and control of a pneumatic morphing soft quadroto…

cs.RO2026

Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments

Fausto Mauricio Lagos Suarez, Akshit Saradagi, Vidya Sumathy +2

This paper addresses the problem of using a deep Reinforcement Learning (RL)-based low-level Quadrotor controller within an autonomous Quadrotor navigation stack for aerial inspect…

cs.RO2026

A Heuristic Approach for Performance Tuning in RL-based Quadrotor Control via Reward Design and Termination Conditions

Fausto Mauricio Lagos Suarez, Akshit Saradagi, Vidya Sumathy +1

Reinforcement learning (RL)-based quadrotor control policies have achieved impressive performance in tasks such as fast navigation in cluttered environments and drone racing, where…

cs.RO2026

Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor

Fausto Mauricio Lagos Suarez, Akshit Saradagi, Vidya Sumathy +2

This article introduces a novel sample-efficient curriculum learning (CL) approach for training an end-to-end reinforcement learning (RL) policy for robust stabilization of a Quadr…