ABC-Net: Semi-Supervised Multimodal GAN-based Engagement Detection using an Affective, Behavioral and Cognitive Model

We present ABC-Net, a novel semi-supervised multimodal GAN framework to detect engagement levels in video conversations based on psychology literature. We use three constructs: behavioral, cognitive, and affective engagement, to extract various features that can effectively capture engagement levels. We feed these features to our semi-supervised GAN network that does regression using these latent representations to obtain the corresponding valence and arousal values, which are then categorized into different levels of engagements. We demonstrate the efficiency of our network through experiments on the RECOLA database. To evaluate our method, we analyze and compare our performance on RECOLA and report a relative performance improvement of more than 5% over the baseline methods. To the best of our knowledge, our approach is the first method to classify engagement based on a multimodal semi-supervised network.

EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle

We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology, our approach combines three interpretations of context for emotion recognition. Our first interpretation is based on using multiple modalities(e.g. faces and gaits) for emotion recognition. For the second interpretation, we gather semantic context from the input image and use a self-attention-based CNN to encode this information. Finally, we use depth maps to model the third interpretation related to socio-dynamic interactions and proximity among agents. We demonstrate the efficiency of our network through experiments on EMOTIC, a benchmark dataset. We report an Average Precision (AP) score of 35.48 across 26 classes, which is an improvement of 7-8 over prior methods. We also introduce a new dataset, GroupWalk, which is a collection of videos captured in multiple real-world settings of people walking. We report an AP of 65.83 across 4 categories on GroupWalk, which is also an improvement over prior methods.

Learning Coordinated Tasks using Reinforcement Learning in Humanoids

With the advent of artificial intelligence and machine learning, humanoid robots are made to learn a variety of skills which humans possess. One of fundamental skills which humans use in day-to-day activities is performing tasks with coordination between both the hands. In case of humanoids, learning such skills require optimal motion planning which includes avoiding collisions with the surroundings. In this paper, we propose a framework to learn coordinated tasks in cluttered environments based on DiGrad - A multi-task reinforcement learning algorithm for continuous action-spaces. Further, we propose an algorithm to smooth the joint space trajectories obtained by the proposed framework in order to reduce the noise instilled during training. The proposed framework was tested on a 27 degrees of freedom (DoF) humanoid with articulated torso for performing coordinated object-reaching task with both the hands in four different environments with varying levels of difficulty. It is observed that the humanoid is able to plan collision free trajectory in real-time. Simulation results also reveal the usefulness of the articulated torso for performing tasks which require coordination between both the arms.

A Deep Reinforcement Learning Approach for Dynamically Stable Inverse Kinematics of Humanoid Robots

Real time calculation of inverse kinematics (IK) with dynamically stable configuration is of high necessity in humanoid robots as they are highly susceptible to lose balance. This paper proposes a methodology to generate joint-space trajectories of stable configurations for solving inverse kinematics using Deep Reinforcement Learning (RL). Our approach is based on the idea of exploring the entire configuration space of the robot and learning the best possible solutions using Deep Deterministic Policy Gradient (DDPG). The proposed strategy was evaluated on the highly articulated upper body of a humanoid model with 27 degree of freedom (DoF). The trained model was able to solve inverse kinematics for the end effectors with 90% accuracy while maintaining the balance in double support phase.