2021

1. Fast Adaptation of Manipulator Trajectories to Task Perturbation by Differentiating through the Optimal Solution MDPI Sensors 2022 [ Abs] [ PDF]

   Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations, such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. Most importantly, however, our algorithm achieves a worst-case speed-up of 160x over the latter approach.

2020

1. Driving the Last Mile: Characterizing and Understanding Distracted Driving Posts on Social Networks AAAI Conference On Web and Social Media (ICWSM) 2020 [ Abs] [ PDF]

   In 2015, 391,000 people were injured due to distracted driv-ing in the US. One of the major reasons behind distracteddriving is the use of cell-phones, accounting for 14% of fatalcrashes. Social media applications have enabled users to stayconnected, however, the use of such applications while driv-ing could have serious repercussions - often leading the userto be distracted from the road and ending up in an accident.In the context of impression management, it has been discov-ered that individuals often take a risk (such as teens smokingcigarettes, indulging in narcotics, and participating in unsafesex) to improve their social standing. Therefore, viewing thephenomena of posting distracted driving posts under the lensof self-presentation, it can be hypothesized that users oftenindulge in risk-taking behavior on social media to improvetheir impression among their peers. In this paper, we firsttry to understand the severity of such social-media-based dis-tractions by analyzing the content posted on a popular socialmedia site where the user is driving and is also simultane-ously creating content. To this end, we build a deep learningclassifier to identify publicly posted content on social mediathat involves the user driving. Furthermore, a framework pro-posed to understand factors behind voluntary risk-taking ac-tivity observes that younger individuals are more willing toperform such activities, and men (as opposed to women) aremore inclined to take risks. Grounding our observations inthis framework, we test these hypotheses on 173 cities acrossthe world. We conduct spatial and temporal analysis on a city-level and understand how distracted driving content postingbehavior changes due to varied demographics. We discoverthat the factors put forth by the framework are significant inestimating the extent of such behavior.

2020

1. Don't cross that stop line: Characterizing Traffic Violations in Metropolitan Cities IEEE International Conference on Multimedia Big Data (BigMM) 2020 [ Abs] [ PDF]

   In modern metropolitan cities, the task of ensuring safe roads is of paramount importance. Automated systems of e-challans (Electronic traffic-violation receipt) are now being deployed across cities to record traffic violations and to issue fines. In the present study, an automated e-challan system established in Ahmedabad (Gujarat, India) has been analyzed for characterizing user behaviour, violation types as well as finding spatial and temporal patterns in the data. We describe a method of collecting e-challan data from the e-challan portal of Ahmedabad traffic police and create a dataset of over 3 million e-challans. The dataset was first analyzed to characterize user behaviour with respect to repeat offenses and fine payment. We demonstrate that a lot of users repeat their offenses (traffic violation) frequently and are less likely to pay fines of higher value. Next, we analyze the data from a spatial and temporal perspective and identify certain spatio-temporal patterns present in our dataset. We find that there is a drastic increase/decrease in the number of e-challans issued during the festival days and identify a few hotspots in the city that have high intensity of traffic violations. In the end, we propose a set of 5 features to model recidivism in traffic violations and train multiple classifiers on our dataset to evaluate the effectiveness of our proposed features. The proposed approach achieves 95% accuracy on the dataset.

2020

1. Are Bots Humans? Analysis of Bot Accounts in 2019 Indian Lok Sabha Elections IEEE International Conference on Multimedia Big Data (BigMM) 2020 [ Abs] [ PDF]

   Social media platforms have taken political and cultural conversations to an online platform making them more accessible. Ability to anonymously post has allowed more people to participate fearlessly. However, this has also led to an opportunity to spread miss information and manipulative content. Political groups around the globe have used Bot accounts to help spread their preferred narrative online during elections. In the midst of 2019 Indian Lok Sabha Elections speculations were made about the presence of cyber-troops/IT Cells which operate fake accounts and push propaganda. Our finding suggests that a portion of Bot accounts seems to be operated by humans in the background. These accounts have a very distinct usage pattern on Twitter compared to legitimate human users. Our experiments also point out that only 1.3% of total interactions are directed from Humans to Bots, showing Bot accounts inability to gel well in the online social network.

2019

1. INFER: INtermediate representations for FuturE pRediction IEEE International Conference on Intelligent Robots and Systems 2019 [ Abs] [ PDF]

   In urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representations (eg. image sequences). But, such methods do not generalize to datasets they have not been trained on. We propose intermediate representations that are particularly well-suited for future prediction. As opposed to using texture (color) information, we rely on semantics and train an autoregressive model to accurately predict future trajectories of traffic participants (vehicles) (see fig. above). We demonstrate that using semantics provides a significant boost over techniques that operate over raw pixel intensities/disparities. Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving). Additionally, we demonstrate an application of our approach in multi-object tracking (data association). To foster further research in transferrable representations and ensure reproducibility, we release all our code and data.