In this paper, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-related states were extracted from the raw tactile signals by a graph-based perception model - TacGNN. The resulting tactile features were then utilized in the policy learning of an in-hand manipulation task in the second stage. This method was examined by a Baoding ball task - simultaneously manipulating two spheres around each other by 180 degrees in hand. We conducted experiments on object states prediction and in-hand manipulation using a reinforcement learning algorithm (PPO). Results show that TacGNN is effective in predicting object-related states during manipulation by decreasing the RMSE of prediction to 0.096cm comparing to other methods, such as MLP, CNN, and GCN. Finally, the robot hand could finish an in-hand manipulation task solely relying on the robotic own perception - tactile sensing and proprioception. In addition, our methods are tested on three tasks with different difficulty levels and transferred to the real robot without further training.

翻译：本文提出一种新颖的触觉灵巧操作学习框架，该框架应用于无视觉传感的拟人化盲机器人手。首先，通过基于图的感知模型TacGNN从原始触觉信号中提取与物体相关的状态；随后，将提取的触觉特征用于第二阶段手内操作任务的策略学习。该方法通过保定球任务（将两个球体在手中同时旋转180度）进行验证。我们采用强化学习算法（PPO）开展了物体状态预测与手内操作实验。结果表明，相较于MLP、CNN和GCN等方法，TacGNN通过将预测均方根误差降至0.096厘米，能有效预测操作过程中的物体相关状态。最终，机器人手可仅依赖自身感知（触觉感知与本体感觉）完成手内操作任务。此外，本方法在三个不同难度级别的任务中完成测试，并成功迁移至真实机器人，无需额外训练。