The ability to predict, anticipate and reason about future outcomes is a key component of
intelligent decision-making systems. In light of the success of deep learning in computer …

In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the
perception and motion forecasting performance of self-driving vehicles. By intelligently …

This article presents a robotic pick-and-place system that is capable of gras** and
recognizing both known and novel objects in cluttered environments. The key new feature of …

This paper proposes a novel tracker which is controlled by sequentially pursuing actions
learned by deep reinforcement learning. In contrast to the existing trackers using deep …

We propose a hierarchical approach for making long-term predictions of future frames. To
avoid inherent compounding errors in recursive pixel-level prediction, we propose to first …

In this paper, we explore how we can build upon the data and models of Internet images and
use them to adapt to robot vision without requiring any extra labels. We present a framework …

In this work, we propose a novel tracking algorithm with real-time performance based on the
'Actor-Critic'framework. This framework consists of two major components:'Actor'and 'Critic' …

We present a new public dataset with a focus on simulating robotic vision tasks in everyday
indoor environments using real imagery. The dataset includes 20,000+ RGB-D images and …

Much of recent research has been devoted to video prediction and generation, yet most of
the previous works have demonstrated only limited success in generating videos on short …

Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot
applications. One of the key challenges is how to explore environments efficiently with …