9 citations · 15 across the 8 of their papers we have counts for
8 papers
Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling
Aman Sinha, Payam Nikdel, Supratik Paul +1
Ensuring the safety of autonomous vehicles (AVs) requires both accurate estimation of their performance and efficient discovery of potential failure cases. This paper introduces Ba…
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula
Eli Bronstein, Sirish Srinivasan, Supratik Paul +4
ML-based motion planning is a promising approach to produce agents that exhibit complex behaviors, and automatically adapt to novel environments. In the context of autonomous drivi…
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
Eli Bronstein, Mark Palatucci, Dominik Notz +14
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL…
DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network
Payam Nikdel, Mohammad Mahdavian, Mo Chen
Human motion prediction is a fundamental part of many human-robot applications. Despite the recent progress in human motion prediction, most studies simplify the problem by predict…
STPOTR: Simultaneous Human Trajectory and Pose Prediction Using a Non-Autoregressive Transformer for Robot Following Ahead
Mohammad Mahdavian, Payam Nikdel, Mahdi TaherAhmadi +1
In this paper, we develop a neural network model to predict future human motion from an observed human motion history. We propose a non-autoregressive transformer architecture to l…
LBGP: Learning Based Goal Planning for Autonomous Following in Front
Payam Nikdel, Richard Vaughan, Mo Chen
This paper investigates a hybrid solution which combines deep reinforcement learning (RL) and classical trajectory planning for the following in front application. Here, an autonom…
Relational Graph Learning for Crowd Navigation
Changan Chen, Sha Hu, Payam Nikdel +2
We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future. Our approa…
Recognizing and Tracking High-Level, Human-Meaningful Navigation Features of Occupancy Grid Maps
Payam Nikdel, Richard Vaughan
This paper describes a system whereby a robot detects and track human-meaningful navigational cues as it navigates in an indoor environment. It is intended as the sensor front-end…