5 papers
AeroGrab: A Unified Framework for Aerial Grasping in Cluttered Environments
Shivansh Pratap Singh, Naveen Sudheer Nair, Samaksh Ujjawal +4
Reliable aerial grasping in cluttered environments remains challenging due to occlusions and collision risks. Existing aerial manipulation pipelines largely rely on centroid-based…
Learn Structure, Adapt on the Fly: Multi-Scale Residual Learning and Online Adaptation for Aerial Manipulators
Samaksh Ujjawal, Naveen Sudheer Nair, Shivansh Pratap Singh +3
Autonomous Aerial Manipulators (AAMs) are inherently coupled, nonlinear systems that exhibit nonstationary and multiscale residual dynamics, particularly during manipulator reconfi…
Physics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics
Rishabh Dev Yadav, Samaksh Ujjawal, Sihao Sun +2
Accurate dynamics models are essential for model-based robotic control, yet nominal Euler--Lagrange models often become inaccurate in the presence of payload variation, unmodeled c…
Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation
Rishabh Dev Yadav, Samaksh Ujjawal, Sihao Sun +2
Accurate dynamics models are critical for aerial manipulators operating under complex tasks such as payload transport. However, modeling these systems remains fundamentally challen…
AERMANI-Diffusion: Regime-Conditioned Diffusion for Dynamics Learning in Aerial Manipulators
Samaksh Ujjawal, Shivansh Pratap Singh, Naveen Sudheer Nair +3
Aerial manipulators undergo rapid, configuration-dependent changes in inertial coupling forces and aerodynamic forces, making accurate dynamics modeling a core challenge for reliab…