Title: Robot Grasping by Exploiting Compliance and Environmental Constraints
Grasping is a crucial skill for any autonomous system that needs to alter the physical world. The complexity of robot grasping stems from the fact that any solution comprises various components: Hand design, control, perception, and planning all affect the success of a grasp. Apart from picking solutions in well-defined industrial scenarios, general grasping in unstructured environment is still an open problem.
In this thesis, we exploit two general properties to devise grasp planning algorithms: the compliance of robot hands and the stiffness of the environment that surrounds an object. We view hand compliance as an enabler for local adaptability in the grasping process that does not require explicit reasoning or planning. As a result, we study compliance-aware algorithms to synthesize grasps. Exploiting hand compliance also simplifies perception, since precise geometric object models are not needed. Complementary to hand compliance is the idea of exploiting the stiffness of the environment. In real-world scenarios, objects never occur in isolation. They are situated in an environmental context: on a table, in a shelf, inside a drawer, etc. Robotic grasp strategies can benefit from contact with the environment by pulling objects to edges, pushing them against surfaces etc. We call this principle the exploitation of environmental constraints. We present grasp planning algorithms which detect and sequence environmental constraint exploitations.
We study the two ideas by focusing on the relationships between the three main constituents of the grasping problem: hand, object, and environment. We show that the interactions between adaptable hands and objects lend themselves to low-dimensional grasp actions. Based on this insight, we devise two grasp planning algorithms which map compliance modes to raw sensor signals using minimal prior knowledge. Next, we focus on the interactions between hand and environment. We show that contacting the environment can improve success in motion and grasping tasks. We conclude our investigations by considering interactions between all three factors: hand, object, and environment. We extend our grasping approach to select the most appropriate environmental constraint exploitation based on the shape of an object. Finally, we consider simple manipulation tasks that require individual finger movements. Although compliant hands pose challenges due to the difficulty in modeling and limitations in sensing, we propose an approach to learn feedback control strategies that solve these tasks. We evaluate all algorithms presented in this thesis in extensive real-world experiments, compare their assumptions and discuss limitations. The investigations and planning algorithms show that exploiting compliance in hands and stiffness in the environment leads to improved grasp performance.