Vinay

Bo Ai, Wei Gao, Vinay et al.

How can a robot navigate successfully in rich and diverse environments, indoors or outdoors, along office corridors or trails on the grassland, on the flat ground or the staircase? To this end, this work aims to address three challenges: (i) complex visual observations, (ii) partial observability of local visual sensing, and (iii) multimodal robot behaviors conditioned on both the local environment and the global navigation objective. We propose to train a neural network (NN) controller for local navigation via imitation learning. To tackle complex visual observations, we extract multi-scale spatial representations through CNNs. To tackle partial observability, we aggregate multi-scale spatial information over time and encode it in LSTMs. To learn multimodal behaviors, we use a separate memory module for each behavior mode. Importantly, we integrate the multiple neural network modules into a unified controller that achieves robust performance for visual navigation in complex, partially observable environments. We implemented the controller on the quadrupedal Spot robot and evaluated it on three challenging tasks: adversarial pedestrian avoidance, blind-spot obstacle avoidance, and elevator riding. The experiments show that the proposed NN architecture significantly improves navigation performance.

Mansha Lamba, Raunak Goswami, Vinay et al.

Our objective is to develop an artificially intelligent system which aims at checking the compatibility between the roommates of same or different sex sharing a common area of residence. There are a few key factors determining one's compatibility with the other person. Interpersonal behaviour , situational awareness, communication skills. Here we are trying to build a system that evaluates user on these key factors not via pen paper test but through a highly engaging set of questions and answers. Hence using these scores as an input to our machine learning algorithm which is based on previous trends to come up with percentage probability of user being compatible with another user. With the growing population there is always a challenge for organisation and educational institutions to make the students and their employees more and more productive and in such cases a person's social environment comes into play. A person may be a genius but as long as he is not able to work well with his peers there will always be a chance of more productive performance. It is a well-established fact that human are and have always been a social animal and this has helped in creating communities of like-minded people. Many times, even when there are a large no of people employed to do a particular task the result may not be as expected as people may not compatible in working with one another. This at the end creates performance gaps, hinders organisation success and in many cases loss of precious resources. Our intent is not to remove the non-compatible people from the picture but to find out the perfect compatible match for the person elsewhere that will not only save the resources will also enable effective use of resources. Through the use of various machine learning classification techniques, we intent to do this.