In-Hand Re-grasp Manipulation with Passive Dynamic Actions via Imitation Learning

Re-grasp manipulation leverages on ergonomic tools to assist humans in accomplishing diverse tasks. In certain scenarios, humans often employ external forces to effortlessly and precisely re-grasp tools like a hammer. Previous development on controllers for in-grasp sliding motion using passive dynamic actions (e.g.,gravity) relies on apprehension of finger-object contact information, and requires customized design for individual objects with varied geometry and weight distribution. It limits their adaptability to diverse objects. In this paper, we propose an end-to-end sliding motion controller based on imitation learning (IL) that necessitates minimal prior knowledge of object mechanics, relying solely on object position information. To expedite training convergence, we utilize a data glove to collect expert data trajectories and train the policy through Generative Adversarial Imitation Learning (GAIL). Simulation results demonstrate the controller's versatility in performing in-hand sliding tasks with objects of varying friction coefficients, geometric shapes, and masses. By migrating to a physical system using visual position estimation, the controller demonstrated an average success rate of 86%, surpassing the baseline algorithm's success rate of 35% of Behavior Cloning(BC) and 20% of Proximal Policy Optimization (PPO).

Robust and Consistent Estimation of Word Embedding for Bangla Language by fine-tuning Word2Vec Model

Word embedding or vector representation of word holds syntactical and semantic characteristics of word which can be an informative feature for any machine learning based models of natural language processing. There are several deep learning based models for the vectorization of words like word2vec, fasttext, gensim, glove etc. In this study, we analysis word2vec model for learning word vectors by tuning different hyper-parameters and present the most effective word embedding for Bangla language. For testing the performances of different word embeddings induced by fine-tuning of word2vec model, we perform both intrinsic and extrinsic evaluations. We cluster the word vectors to examine the relational similarity of words and also use different word embeddings as the feature of news article classifier for extrinsic evaluation. From our experiment, we discover that the word vectors with 300 dimension, generated from 'skip-gram' method of word2vec model using the sliding window size of 4, are giving the most robust vector representations for Bangla language.

A Dynamic Modelling Framework for Human Hand Gesture Task Recognition

Gesture recognition and hand motion tracking are important tasks in advanced gesture based interaction systems. In this paper, we propose to apply a sliding windows filtering approach to sample the incoming streams of data from data gloves and a decision tree model to recognize the gestures in real time for a manual grafting operation of a vegetable seedling propagation facility. The sequence of these recognized gestures defines the tasks that are taking place, which helps to evaluate individuals' performances and to identify any bottlenecks in real time. In this work, two pairs of data gloves are utilized, which reports the location of the fingers, hands, and wrists wirelessly (i.e., via Bluetooth). To evaluate the performance of the proposed framework, a preliminary experiment was conducted in multiple lab settings of tomato grafting operations, where multiple subjects wear the data gloves while performing different tasks. Our results show an accuracy of 91% on average, in terms of gesture recognition in real time by employing our proposed framework.