Prediction of Human Trajectory Following a Haptic Robotic Guide Using Recurrent Neural Networks

Social intelligence is an important requirement for enabling robots to collaborate with people. In particular, human path prediction is an essential capability for robots in that it prevents potential collision with a human and allows the robot to safely make larger movements. In this paper, we present a method for predicting the trajectory of a human who follows a haptic robotic guide without using sight, which is valuable for assistive robots that aid the visually impaired. We apply a deep learning method based on recurrent neural networks using multimodal data: (1) human trajectory, (2) movement of the robotic guide, (3) haptic input data measured from the physical interaction between the human and the robot, (4) human depth data. We collected actual human trajectory and multimodal response data through indoor experiments. Our model outperformed the baseline result while using only the robot data with the observed human trajectory, and it shows even better results when using additional haptic and depth data.Comment: 6 pages, Submitted to IEEE World Haptics Conference 201

Depolymerization and decolorization of chitosan by ozone treatment

Currently, depolymerization and decolorization of chitosan are achieved by chemical or enzymatic methods which are time consuming and expensive. Ozone has been shown to be able to degrade macromolecules and remove pigments due to its high oxidation potential. In this study, the effects of ozone treatment on depolymerization and decolorization of chitosan were investigated. Crawfish chitosan was ozonated in water and acetic acid solution for 0, 5, 10, 15, and 20 minutes at room temperature with 12wt% gas. For the determination of viscosity–average Molecular weight of chitosan, an ubbelohde viscometer was used to measure the intrinsic viscosity, and the Mark-Houwink equation was used to calculate molecular weight. Color of ozone-treated chitosan was analyzed using a Minolta spectrophotometer. The degree of deacetylation was determined by a colloid titration method. Molecular weight of ozone-treated chitosan in acetic acid solution decreased appreciably as the ozone treatment time increased. Ozonation for 20 minutes reduced the molecular weight of the chitosan by 92% (104 KDa) compared to the untreated chitosan (1333 KDa) with a decrease in viscosity of the chitosan solution. Ozonation for 5 min markedly increased the whiteness of chitosan; however, further ozonation resulted in development of yellowness. In case of the ozonation in water, there were no significant differences of the molecular weight and color between ozone-treated chitosans. However, results showed that ozone treatment of chitosan in both water and acetic acid solution was not effective in removing acetyl groups (deacetylation) in chitosan molecules. This study showed that ozone can be used to modify molecular weight and remove pigments of chitosan without chemical use in a shorter time with less cost

Data-Mining Approach to Analyzing Industrial Process Information for Real-Time Monitoring

This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults

Thermal activation energy of 3D vortex matter in NaFe1-xCoxAs (x=0.01, 0.03 and 0.07) single crystals

We report on the thermally activated flux flow dependency on the doping dependent mixed state in NaFe1-xCoxAs (x=0.01, 0.03, and 0.07) crystals using the magnetoresistivity in the case of B//c-axis and B//ab-plane. It was found clearly that irrespective of the doping ratio, magnetoresistivity showed a distinct tail just above the Tc, offset associated with the thermally activated flux flow (TAFF) in our crystals. Furthermore, in TAFF region the temperature dependence of the activation energy follows the relation U(T, B)=U_0 (B) (1-T/T_c )^q with q=1.5 in all studied crystals. The magnetic field dependence of the activation energy follows a power law of U_0 (B)~B^(-{\alpha}) where the exponent {\alpha} is changed from a low value to a high value at a crossover field of B=~2T, indicating the transition from collective to plastic pinning in the crystals. Finally, it is suggested that the 3D vortex phase is the dominant phase in the low-temperature region as compared to the TAFF region in our series samples

Neural Attention Memory

We propose a novel perspective of the attention mechanism by reinventing it as a memory architecture for neural networks, namely Neural Attention Memory (NAM). NAM is a memory structure that is both readable and writable via differentiable linear algebra operations. We explore three use cases of NAM: memory-augmented neural network (MANN), few-shot learning, and efficient long-range attention. First, we design two NAM-based MANNs of Long Short-term Memory (LSAM) and NAM Turing Machine (NAM-TM) that show better computational powers in algorithmic zero-shot generalization tasks compared to other baselines such as differentiable neural computer (DNC). Next, we apply NAM to the N-way K-shot learning task and show that it is more effective at reducing false positives compared to the baseline cosine classifier. Finally, we implement an efficient Transformer with NAM and evaluate it with long-range arena tasks to show that NAM can be an efficient and effective alternative for scaled dot-product attention.Comment: Preprint. Under revie

Sample-Efficient Training of Robotic Guide Using Human Path Prediction Network

Training a robot that engages with people is challenging, because it is expensive to involve people in a robot training process requiring numerous data samples. This paper proposes a human path prediction network (HPPN) and an evolution strategy-based robot training method using virtual human movements generated by the HPPN, which compensates for this sample inefficiency problem. We applied the proposed method to the training of a robotic guide for visually impaired people, which was designed to collect multimodal human response data and reflect such data when selecting the robot's actions. We collected 1,507 real-world episodes for training the HPPN and then generated over 100,000 virtual episodes for training the robot policy. User test results indicate that our trained robot accurately guides blindfolded participants along a goal path. In addition, by the designed reward to pursue both guidance accuracy and human comfort during the robot policy training process, our robot leads to improved smoothness in human motion while maintaining the accuracy of the guidance. This sample-efficient training method is expected to be widely applicable to all robots and computing machinery that physically interact with humans