用伪触觉反馈解决抓取状态模糊，让纯仿真训练的机器人更可靠。
Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning
- 用力控夹爪模拟触觉信号，生成无噪声的抓取状态反馈
- 在三个真实任务中实现无需实测数据的高效抓取
- 适合想跳过真实数据采集的仿真学习研究者
抓取任务是机器人与环境交互的基础，但夹爪状态模糊会显著降低模仿学习策略的鲁棒性。数据驱动方法面临真实世界数据成本高的问题，而仿真数据虽低成本，却受限于仿真到现实的差距。我们发现夹爪状态模糊的根本原因在于缺乏触觉反馈。为此，提出一种新方法：借鉴力控夹爪作为触觉传感器的思想，引入伪触觉反馈。该方法无需额外数据收集或硬件改动，即可增强策略鲁棒性，并为策略提供无噪声的二值夹爪状态观测，从而支持纯仿真学习，充分发挥仿真潜力。在三个真实抓取任务上的实验验证了该方法的必要性、有效性与高效性。
原文摘要 · Abstract (English)
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.
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