Navyata Sanghvi

Navyata Sanghvi, Shinnosuke Usami, Mohit Sharma et al.

Various methods for solving the inverse reinforcement learning (IRL) problem have been developed independently in machine learning and economics. In particular, the method of Maximum Causal Entropy IRL is based on the perspective of entropy maximization, while related advances in the field of economics instead assume the existence of unobserved action shocks to explain expert behavior (Nested Fixed Point Algorithm, Conditional Choice Probability method, Nested Pseudo-Likelihood Algorithm). In this work, we make previously unknown connections between these related methods from both fields. We achieve this by showing that they all belong to a class of optimization problems, characterized by a common form of the objective, the associated policy and the objective gradient. We demonstrate key computational and algorithmic differences which arise between the methods due to an approximation of the optimal soft value function, and describe how this leads to more efficient algorithms. Using insights which emerge from our study of this class of optimization problems, we identify various problem scenarios and investigate each method's suitability for these problems.

Harsh Agarwal, Navyata Sanghvi, Vivek Roy et al.

Accurate smartphone localization (< 1-meter error) for indoor navigation using only RSSI received from a set of BLE beacons remains a challenging problem, due to the inherent noise of RSSI measurements. To overcome the large variance in RSSI measurements, we propose a data-driven approach that uses a deep recurrent network, DeepBLE, to localize the smartphone using RSSI measured from multiple beacons in an environment. In particular, we focus on the ability of our approach to generalize across many smartphone brands (e.g., Apple, Samsung) and models (e.g., iPhone 8, S10). Towards this end, we collect a large-scale dataset of 15 hours of smartphone data, which consists of over 50,000 BLE beacon RSSI measurements collected from 47 beacons in a single building using 15 different popular smartphone models, along with precise 2D location annotations. Our experiments show that there is a very high variability of RSSI measurements across smartphone models (especially across brand), making it very difficult to apply supervised learning using only a subset of smartphone models. To address this challenge, we propose a novel statistic similarity loss (SSL) which enables our model to generalize to unseen phones using a semi-supervised learning approach. For known phones, the iPhone XR achieves the best mean distance error of 0.84 meters. For unknown phones, the Huawei Mate20 Pro shows the greatest improvement, cutting error by over 38\% from 2.62 meters to 1.63 meters error using our semi-supervised adaptation method.

Navyata Sanghvi, Ryo Yonetani, Kris Kitani

Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a $\mathbf{computational\; model}$ of interactions in a social environment of multiple agents and multiple groups. The Multiagent Group Perception and Interaction (MGpi) network is a deep neural network that predicts the appropriate social action to execute in a group conversation (e.g., speak, listen, respond, leave), taking into account neighbors' observable features (e.g., location of people, gaze orientation, distraction, etc.). A central component of MGpi is the Kinesic-Proxemic-Message (KPM) gate, that performs social signal gating to extract important information from a group conversation. In particular, KPM gate filters incoming social cues from nearby agents by observing their body gestures (kinesics) and spatial behavior (proxemics). The MGpi network and its KPM gate are learned via imitation learning, using demonstrations from our designed $\mathbf{social\; interaction\; simulator}$. Further, we demonstrate the efficacy of the KPM gate as a social attention mechanism, achieving state-of-the-art performance on the task of $\mathbf{group\; identification}$ without using explicit group annotations, layout assumptions, or manually chosen parameters.