A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centred design philosophy are highly desired in today’s robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase usability and acceptability of robots by the users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviours for robots, e.g., in physical HRI and tele-operation. To this end, we present a novel robot learning framework based on Dynamic Movement Primitives (DMPs), taking into consideration both the positional and the contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter Robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table

Temporal Patterns in Multi-modal Social Interaction between Elderly Users and Service Robot

Social interaction, especially for older people living alone is a challenge currently facing human-robot interaction (HRI). User interfaces to manage service robots in home environments need to be tailored for older people. Multi-modal interfaces providing users with more than one communication option seem promising. There has been little research on user preference towards HRI interfaces; most studies have focused on utility and functionality of the interface. In this paper, we took both objective observations and participants’ opinions into account in studying older users with a robot partner. Our study was under the framework of the EU FP7 Robot-Era Project. The developed dual-modal robot interface offered older users options of speech or touch screen to perform tasks. Fifteen people aged from 70 to 89 years old, participated. We analyzed the spontaneous actions of the participants, including their attentional activities (eye contacts) and conversational activities, the temporal characteristics (timestamps, duration of events, event transitions) of these social behaviours, as well as questionnaires. This combination of data distinguishes it from other studies that focused on questionnaire ratings only. There were three main findings. First, the design of the Robot-Era interface was very acceptable for older users. Secondly, most older people used both speech and tablet to perform the food delivery service, with no difference in their preferences towards either. Thirdly, these older people had frequent and long-duration eye contact with the robot during their conversations, showing patience when expecting the robot to respond. They enjoyed the service. Overall, social engagement with the robot demonstrated by older people was no different from what might be expected towards a human partner. This study is an early attempt to reveal the social connections between human beings and a personal robot in real life. Our observations and findings should inspire new insights in HRI research and eventually contribute to next-generation intelligent robot developmen

A Modified Sequence-to-point HVAC Load Disaggregation Algorithm

This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used as inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability

Polyculture and Monoculture Affect the Fitness, Behavior and Detoxification Metabolism of Bemisia tabaci (Hemiptera: Aleyrodidae)

Herbivores respond differently to the level of plant diversity encountered. Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) are highly polyphagous herbivores which cause considerable damage to various crops. Herein, we reared this species both in polyculture and monoculture, including preferred and less preferred host plants such as Chinese cabbage (Brassica rapa L.), tomato (Solanum lycopersicum L.), kidney bean (Phaseolus vulgaris L.) and summer squash (Cucurbita pepo L.). Trends in survival and oviposition were recorded, and impact of plants on growth and development of B. tabaci were studied, particularly in terms of detoxification and digestive enzymatic activity in the insects. We found that the survival rate was the highest in Chinese cabbage monoculture treatment. Further, the egg numbers on individual species in the polyculture generally reflected numbers on the same plant species in monoculture. However, more eggs were observed in each of the four plant species tested in the context of polyculture. The activity of superoxide dismutases (SOD) and alkaline phosphatase (AKP) in B. tabaci fed in a choice situation were significantly lower than those fed with tomato monoculture, indicating a dilution of toxicity with a multi-plant diet compared with less preferred host plant diet. Also, the survival rate of B. tabaci in monoculture was negatively correlated with SOD amount of whitefly. In the plants attacked by whiteflies, the activity of polyphenol oxidase (PPO) and catalase (CAT) in Chinese cabbage was lower in polyculture than in the monoculture. These results implied that multi-plant treatments contained fewer secondary metabolite substances and might be less toxic to polyphagous herbivores. As such, the work herein contributes knowledge relevant for more effective control and management of B. tabaci