Multi-Fingered End-Effector Grasp Reflex Modeling for One-Shot Tactile Servoing in Tool Manipulation Tasks

Autonomous tool manipulation tasks are challenging for robots because they must reason over the tool’s object affordances, how to grasp the tool so it may be used, how the tool will interact with other objects in the environment, and how to perform the complex tool affordances to complete the manipulation task. Focusing on tool grasping presents further challenges, specifically generalization to novel tools and modeling the problem in an explainable way suitable for safety-critical task domains, such as robots operating autonomously to perform repair tasks in NASA lunar habitats. In this work, we focus on grasping tools in an explainable way that can be generalized to novel tools. We present a logistic regression based grasp reflex model, which maps continuous end-effector sensor data to a set of discrete symbolic states. An adjustment policy uses these symbolic states to compute the appropriate gradient to change the end-effector pose and increase the probability of a secure tool grasp. Once the tool grasp is sufficiently secure, the robot proceeds with the rest of the manipulation task. We test our grasp reflex model on 6 novel tools, and find that the model achieves one-shot generalization by successfully using tactile servoing to secure grasps from one example of a secure grasp state. The robot’s ability to learn to grasp tools in an explainable way that achieves one-shot generalization to novel tools demonstrates the power of our grasp reflex model in allowing robots to achieve autonomous tool manipulation tasks.