AIREC-Basic: Consistent Demonstration Data Collection for Imitation Learning with Redundant Robot Arms

Abstract

The performance of robotic imitation learning (IL) largely depends on the quality of human demonstrations. To address this challenge, we present AIREC-Basic, a leader–follower teleoperation system equipped with a dual-arm mobile manipulator that enables efficient data collection. The system employs 8-DoF redundant arms to realize diverse task postures, but such redundancy can reduce the consistency of demonstrations. To overcome this issue, we propose a novel control strategy, Soft Homing Control (SHC), which mitigates redundancy while preserving intuitive operator control, thereby improving dataset consistency. We validate our approach on three household tasks using state-of-the-art IL algorithms (ACT, Diffusion Policy, and HSARNN). Experimental results show that SHC significantly reduces joint trajectory variance and improves task success rates, particularly in scenarios with strong trajectory constraints and frequent contacts.