arXiv:2610.29511v1 Announce Type: new Abstract: Haptic teleoperation requires low-latency and reliable communication to preserve stable and responsive interaction between a human operator and a remote environment. However, network congestion can strongly degrade teleoperation performance, particularly when haptic traffic competes with other data flows for limited radio resources. This work investigates a deep reinforcement learning (DRL)-assisted teleoperation framework over an Ericsson 5G standalone (SA) testbed under different congestion and quality-of-service (QoS) configurations. In the evaluated task, a human operator uses a haptic device to steer a spaceship through a remote virtual game. The DRL agent runs inside the remote environment, hosted at the network edge, and assists the operator by blending its own actions with the operator's commands received over the 5G network. This placement gives the agent direct access to the current state of the environment, unaffected by network delay. Experimental results show that DRL assistance improves task performance under all network conditions, with the largest benefit under congestion, where it reduces mean task completion time by 20.6%. Under congestion, assisted operation even outperforms unassisted operation with QoS prioritisation, which restores latency close to the uncongested baseline.