This paper proposes a developmental learning model for joint attention between a robot and a human caregiver. The proposed model has abilities to accelerate the learning and improve the final task performance owing to two kinds of developments: a robot’s development and a caregiver’s one. The robot’s development means that the sensing and the actuating capabilities of the robot change from imma...

Applying positive joint torque feedback in direct drive motor systems reduces the eeect of external load torques. This is most beneecial when accurate position tracking is required in the presence of large disturbances or for complex loads as found in robotic manip-ulators. We formulate the problem of torque feedback in the presence of actuator dynamics. The problem of nding the optimal torque ...

We present a semantic role labeling system submitted to the closed track of the CoNLL-2005 shared task. The system, introduced in (Toutanova et al., 2005), implements a joint model that captures dependencies among arguments of a predicate using log-linear models in a discriminative re-ranking framework. We also describe experiments aimed at increasing the robustness of the system in the presenc...

Given several tasks, multi-task learning (MTL) learns multiple tasks jointly by exploring the interdependence between them. The basic assumption in MTL is that those tasks are indeed related. Existing MTL methods model the task relatedness/interdependence in two different ways, either common parameter-sharing or common featuresharing across tasks. In this paper, we propose a novel multi-task le...

Topic sentiment joint model is an extended model which aims to deal with the problem of detecting sentiments and topics simultaneously from online reviews. Most of existing topic sentiment joint modeling algorithms infer resulting distributions from the co-occurrence of words. But when the training corpus is short and small, the resulting distributions might be not very satisfying. In this pape...

We address the estimation of biomechanical parameters with wearable measurement technologies. In particular, we focus on the estimation of sagittal plane ankle joint stiffness in dorsiflexion/plantar flexion. For this estimation, a novel nonlinear biomechanical model of the lower leg was formulated that is driven by electromyographic signals. The model incorporates a two-dimensional kinematic d...

To alleviate the error propagation in the traditional pipelined models for Abstract Meaning Representation (AMR) parsing, we formulate AMR parsing as a joint task that performs the two subtasks: concept identification and relation identification simultaneously. To this end, we first develop a novel componentwise beam search algorithm for relation identification in an incremental fashion, and th...

A nonlinear autoregressive moving average with exogenous input (NARMAX) model structure is used to develop paraplegic dynamic leg joint model and the result is compared to a model built using artificial neural network (ANN). A series of experiments employing Functional Electrical Stimulation (FES) with varying pulse width are conducted to determine the impact of the leg swing angle. The data ob...