Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the robustness of imitation learning policies for these tasks. Data-driven solutions face the challenge of high real-world data costs, while simulation data, despite its low costs, is limited by the sim-to-real gap. We identify the root cause of gripper state ambiguity as the lack of tactile feedback. To address this, we propose a novel approach employing pseudo-tactile as feedback, inspired by the idea of using a force-controlled gripper as a tactile sensor. This method enhances policy robustness without additional data collection and hardware involvement, while providing a noise-free binary gripper state observation for the policy and thus facilitating pure simulation learning to unleash the power of simulation. Experimental results across three real-world grasp-based tasks demonstrate the necessity, effectiveness, and efficiency of our approach.

翻译：抓握式操作任务是机器人与环境交互的基础，然而夹持器状态模糊性显著降低了此类任务中模仿学习策略的鲁棒性。数据驱动方法面临现实世界数据成本高昂的挑战，而仿真数据尽管成本较低，却受限于仿真到现实的差距。我们将夹持器状态模糊性的根本原因归结为触觉反馈的缺失。为此，受将力控夹持器用作触觉传感器的思想启发，我们提出一种采用伪触觉作为反馈的新方法。该方法无需额外数据采集与硬件介入即可提升策略鲁棒性，同时为策略提供无噪声的二进制夹持器状态观测，从而促进纯仿真学习以释放仿真的潜力。在三个现实世界抓握任务上的实验结果验证了我们方法的必要性、有效性与高效性。