Passive Haptic Learning (PHL) is the acquisition of sensorimotor skills with little or no active attention to learning. This technique is facilitated by wearable computing, and applications are diverse. However, it is not known whether rhythmbased information can be conveyed passively. In a 12 participant study, we investigate whether Morse code, a rhythmbased text entry system, can be learned through PHL using the bone conduction transducer on Google Glass. After four hours of exposure to passive stimuli while focusing their attention on a distraction task, PHL participants achieved a 94% accuracy rate keying a pangram (a phrase with all the letters of the alphabet) using Morse code on Glass's trackpad versus 53% for the control group. Most PHL participants achieved 100% accuracy before the end of the study. In written tests, PHL participants could write the codes for each letter of the alphabet with 98% accuracy versus 59% for control. When perceiving Morse code, PHL participants also performed significantly better than control: 83% versus 46% accuracy.
We present a novel algorithm that solves the distributor's pallet packing problem. In contrast to existing algorithms, our method optimizes stack stability in addition to stack volume. Furthermore, our algorithm explicitly handles cases where the construction of homogeneous layers of packages with equal height is impossible due to differences in package heights and quantities. The algorithm is a nested beam search that separately optimizes local and global evaluation criteria. We show successful results on both real world and synthetic data sets, compare our performance to an existing algorithm and demonstrate experimental applications in simulation and on a real palletizing robot.
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