Joint torques prediction of a robotic arm using neural networks

arXiv:2405.00695v1 Announce Type: cross
Abstract: Accurate dynamic models are crucial for many robotic applications. Traditional approaches to deriving these models are based on the application of Lagrangian or Newtonian mechanics. Although these methods provide a good insight into the physical behaviour of the system, they rely on the exact knowledge of parameters such as inertia, friction and joint flexibility. In addition, the system is often affected by uncertain and nonlinear effects, such as saturation and dead zones, which can be …

abstract application applications arm arxiv cs.lg cs.ro dynamic good insight knowledge networks neural networks prediction robotic robotic arm type