Physical Human Interactive Guidance: Identifying Grasping Principles from Human-Planned Grasps

Abstract

We present a novel and simple experimental method called physical human interactive guidance to study humanplanned grasping. Instead of studying how the human uses his/her own biological hand or how a human teleoperates a robot hand in a grasping task, the method involves a human interacting physically with a robot arm and hand, carefully moving and guiding the robot into the grasping pose, while the robot's configuration is recorded. Analysis of the grasps from this simple method has produced two interesting results. First, the grasps produced by this method perform better than grasps generated through a state-of-the-art automated grasp planner. Second, this method when combined with a detailed statistical analysis using a variety of grasp measures (physics-based heuristics considered critical for a good grasp) offered insights into how the human grasping method is similar or different from automated grasping synthesis techniques. Specifically, data from the physical human interactive guidance method showed that the human-planned grasping method provides grasps that are similar to grasps from a state-of-the-art automated grasp planner, but differed in one key aspect. The robot wrists were aligned with the object's principal axes in the human-planned grasps (termed low skewness in this paper), while the automated grasps used arbitrary wrist orientation. Preliminary tests show that grasps with low skewness were significantly more robust than grasps with high skewness (77-93%). We conclude with a detailed discussion of how the physical human interactive guidance method relates to existing methods to extract the human principles for physical interaction.

The Next Step in Robot Commissioning: Autonomous Picking and Palletizing

“…Opposed to the similarly defined representations in [16], which are learned in simulation, we deliberately design grasp envelopes to incorporate prior knowledge about robust grasp poses in our representation. In a concept originating from observations of human grasping behavior, it has been shown that the grasping device should be roughly aligned with the target object's principal components to achieve robust grasps [25]. For the case depicted in Fig.…”

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“…In addition, we constrain the gripper orientation to roughly align with the principal components of the inner cylinder: i. e., the gripper's approach axis to point towards the cylinder axis and the gripper's vertical axis to be parallel with the cylinder axis. These constraints are directly motivated by research showing that humans tend to grasp objects by aligning their hand with the principal components of the target object [10]. Finally, the cylindrical shell defined in this manner can be truncated by imposing additional planar constraints, in order to exclude undesirable regions such as ones in which the gripper would collide with the environment.…”

“…Instead of relying on pre-planned object-specific grasps, we leverage a constraint-based grasp formulation which allows us to exploit redundancies in the grasping task and to incorporate knowledge of human grasping principles [10]. We introduce the term grasp envelope to refer to a set of constraints on a grasping device's posture (i.e., wrist position/orientation and hand joint values).…”

Robot grasp detection using multimodal deep convolutional neural networks

“…However, a major issue is that the algorithms are only tested on simulated images, ignoring complicated real-world objects. Recent methods 9,10 find that the generic grasping measurement method in simulation is not a precise description for successful real-world object grasping. More importantly, generalization capabilities of these full model-based methods are a major concern because of the numerous unpredictable model objects in our daily life.…”