Environment-constrained grasping exploits beneficial interactions between hand, object, and environment to increase grasp success. Instead of focusing on the final static relationship between hand posture and object pose, this view of grasping emphasizes the need and the opportunity to select the most appropriate, contact-rich grasping motion, leading up to a final static grasp configuration. This view changes the nature of the underlying planning problem: Instead of planning for static contact points, we need to decide which environmental constraint (EC) to use during the grasping motion. We propose a method to make these decisions based on depth measurements so as to generate robust grasps for a large variety of objects. Our planner exploits the advantages of a soft robot hand and learns a hand-specific classifier for edge-, surface-, and wall-grasps, each exploiting a different EC. Additionally, we show how the model can continuously be improved in a contextual multi-armed bandit setting without an explicit training and test phase, enabling the continuous improvement of a robot's grasping skills throughout life time.
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