Task-Oriented Grasping (TOG) presents a significant challenge, requiring a nuanced understanding of task semantics, object affordances, and the functional constraints dictating how an object should be grasped for a specific task. To address these challenges, we introduce GRIM (Grasp Re-alignment via Iterative Matching), a novel training-free framework for task-oriented grasping. Initially, a coarse alignment strategy is developed using a combination of geometric cues and principal component analysis (PCA)-reduced DINO features for similarity scoring. Subsequently, the full grasp pose associated with the retrieved memory instance is transferred to the aligned scene object and further refined against a set of task-agnostic, geometrically stable grasps generated for the scene object, prioritizing task compatibility. In contrast to existing learning-based methods, GRIM demonstrates strong generalization capabilities, achieving robust performance with only a small number of conditioning examples.

Abstract Predicting human motion based on past observed motion is one of the challenging issues in computer vision and graphics. Existing research works are dealing with this issue by using discriminative models and showing the results for cases that follow a homogeneous distribution (in distribution) and not discussing the issues of the domain shift problem, where training and testing data follow a heterogeneous (out of distribution) problem, which is the reality when such models are used in practice. However, recent research proposed addressing domain shift issues by augmenting the discriminative model with a generative model and obtained better results. In the present investigation, we propose regularizing the extended network by inserting linear layers to minimize the rank of the latent space and train the entire end-to-end network. We regularize the network to strengthen the model to deal effectively with domain shift scenarios. Both training and testing data come from different distribution sets; to deal with this, we toughen our network by adding the extra linear layers to the network encoder. We tested our model with the benchmark datasets, CMU Motion Capture and Human3.6M, and proved that our model outperforms 14 OoD actions of H3.6M and 7 OoD actions of CMU MoCap in terms of the Euclidean distance calculated between predicted and ground truth joint angle values. Our average results of 14 OoD actions for short-term (80, 160, 320, 400) are 0.34, 0.6, 0.96, 1.07, and for CMU MoCap of 7 OoD actions for short-term and long term (80, 160, 320, 400, 1000) are 0.28, 0.45, 0.77, 0.89, 1.46. All these results are much better than the other state-of-the-art results.

Electrodermal activity (EDA) is indicative of psychological processes related to human cognition and emotions. Previous research has studied many methods for extracting EDA features; however, their appropriateness for emotion recognition has been tested using a small number of distinct feature sets and on different, usually small, data sets. In the current research, we reviewed 25 studies and implemented 40 different EDA features across time, frequency and time-frequency domains on the publicly available AMIGOS dataset. We performed a systematic comparison of these EDA features using three feature selection methods, Joint Mutual Information (JMI), Conditional Mutual Information Maximization (CMIM) and Double Input Symmetrical Relevance (DISR) and machine learning techniques. We found that approximately the same numbers of features are required to obtain the optimal accuracy for the arousal recognition and the valence recognition. Also, the subject-dependent classification results were significantly higher than the subject-independent classification for both arousal and valence recognition. Statistical features related to the Mel-Frequency Cepstral Coefficients (MFCC) were explored for the first time for the emotion recognition from EDA signals and they outperformed all other feature groups, including the most commonly used Skin Conductance Response (SCR) related features.

In this paper, we have designed the vector fields (VFs) for all the six joints (hip, knee, and ankle) of a bipedal walking model. The bipedal gait is the manifestation of temporal changes in the six joints angles, two each for hip, knee, and ankle values and it is a combination of seven different discrete subphases. Developing the correct joint trajectories for all the six joints was difficult from a purely mechanics-based model due to its inherent complexities. To get the correct and exact joint trajectories, it is very essential for a modern bipedal robot to walk stably. By designing the VF correctly, we are able to get the stable joint trajectory ranges and able to reproduce angle ranges from theses designed VFs. This is purely a data driven computational modeling approach, which is based on the hypothesis that morphologically similar structure (human-robot) can adopt similar gait patterns. To validate the correctness of the design, we have applied all the possible combination of joint trajectories to HOAP-2 bipedal robot, which could walk successfully maintaining its stability. The VF provides joint trajectories for a particular joint. The results show that our data driven computational model is able to provide the correct joints angle ranges, which are stable.

Exploring correct patterns from low‐frequency time‐series data is challenging. For resolving this problem, the concept of possibility theory–based hidden Markov model (PTBHMM) has been proposed. In this article, all three fundamental problems (evaluation, decoding, and learning) of conventional HMM have been addressed using possibility theory. For handling uncertainty, we have used an axiomatic approach of possibility theory proposed by Zadeh. The time complexity of existing solutions of HMM (forward, backward, Viterbi, and Baum Welch) and proposed possibility‐based solutions has been calculated and compared. From the comparison result, it has been found that PTBHMM has lesser time complexity and hence will be more suitable for real‐time gesture–based communication.

The visual perception of eyewitness plays a vital role in criminal identification scenario. It helps law enforcement authorities in searching particular criminal from their previous record. It has been reported that searching a criminal record manually requires too much time to get the accurate result. We have proposed a query-based approach which minimises the computational cost along with the reduction of search space. A symbolic database has been created to perform a stringent analysis on 150 public (Bollywood celebrities and Indian cricketers) and 90 local faces (our data-set). An expert knowledge has been captured to encapsulate every criminal’s anatomical and facial attributes in the form of symbolic representation. A fast query-based searching strategy has been implemented using dynamic decision tree data structure which allows four levels of decomposition to fetch respective criminal records. Two types of case studies - viewed and forensic sketches have been considered to evaluate the strength of our proposed approach. We have derived 1200 views of the entire population by taking into consideration 80 participants as eyewitness. The system demonstrates an accuracy level of 98.6% for test case I and 97.8% for test case II. It has also been reported that experimental results reduce the search space up to 30 most relevant records.

The objective of the work presented here is to develop a low cost active knee prosthetic devices as real time embedded system which utilizes the available biological motor control circuit properly integrated with a central pattern generator (CPG) aided control scheme. The approach is completely different from the existing Active Prosthetic devices, designed primarily as stand alone systems utilizing multiple sensors and embedded rigid control schemes. First we analyzed a fuzzy logic based methodology for offering suitable gait for an amputee, followed by formulating a suitable algorithm for designing a CPG, based on Rayleighpsilas oscillator. Using the oscillator we presented a number of simulation results which showed the behavior of knee angles and hip angles and determined the stable limit cycles of the network, and compared them with the captured gaits of an individual. Subsequently, we presented a methodology about how to use CPG outputs for calculating the damping profile for controlling a prosthetic device called AMAL (adaptive modular active leg).