Learning-Based Modular Task-Oriented Grasp Stability Assessment

Assessing grasp stability is essential to prevent the failure of robotic manipulation tasks due to sensory data and object uncertainties. Learning-based approaches are widely deployed to infer the success of a grasp. Typically, the underlying model used to estimate the grasp stability is trained for a specific task, such as lifting, hand-over, or pouring. Since every task has individual stability demands, it is important to adapt the trained model to new manipulation actions. If the same trained model is directly applied to a new task, unnecessary grasp adaptations might be triggered, or in the worst case, the manipulation might fail. To address this issue, we divide the manipulation task used for training into seven sub-tasks, defined as modular tasks. We deploy a learning-based approach and assess the stability for each modular task separately. We further propose analytical features to reduce the dimensionality and the redundancy of the tactile sensor readings. A main task can thereby be represented as a sequence of relevant modular tasks. The stability prediction of the main task is computed based on the inferred success labels of the modular tasks. Our experimental evaluation shows that the proposed feature set lowers the prediction error up to 5.69% compared to other sets used in state-of-the-art methods. Robotic experiments demonstrate that our modular task-oriented stability assessment avoids unnecessary grasp force adaptations and regrasps for various manipulation tasks.