5 papers
PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers
Koyo Fujii, Luis Figueredo, Praminda Caleb-Solly +4
Accurately estimating object mass and friction is fundamental to reliable robotic manipulation. While interactive perception is powerful, most approaches rely on specialized hardwa…
DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties
Oussama Zaim, Mélodie Daniel, Aly Magassouba +2
Robust deployment of deep reinforcement learning (DRL) policies on real robots remains challenging due to discrepancies between simulation and real-world dynamics. We address this…
ManeuverNet: A Soft Actor-Critic Framework for Precise Maneuvering of Double-Ackermann-Steering Robots with Optimized Reward Functions
Kohio Deflesselle, Mélodie Daniel, Aly Magassouba +2
Autonomous control of double-Ackermann-steering robots is essential in agricultural applications, where robots must execute precise and complex maneuvers within a limited space. Cl…
Towards Safe Maneuvering of Double-Ackermann-Steering Robots with a Soft Actor-Critic Framework
Kohio Deflesselle, Mélodie Daniel, Aly Magassouba +2
We present a deep reinforcement learning framework based on Soft Actor-Critic (SAC) for safe and precise maneuvering of double-Ackermann-steering mobile robots (DASMRs). Unlike hol…
Beyond Detection -- Orchestrating Human-Robot-Robot Assistance via an Internet of Robotic Things Paradigm
Joseph Hunt, Koyo Fujii, Aly Magassouba +1
Hospital patient falls remain a critical and costly challenge worldwide. While conventional fall prevention systems typically rely on post-fall detection or reactive alerts, they a…