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Many Perception Tasks are Highly Redundant Functions of their Input Data
We show that many perception tasks, from visual recognition, semantic segmentation, optical flow, depth estimation to vocalization discrimination, are highly redundant functions of their input data.
EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks
Event-based sensors have recently drawn increasing interest in robotic perception due to their lower latency, higher dynamic range, and lower bandwidth requirements compared to standard CMOS-based imagers.
Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors
This paper presents a Dynamic Vision Sensor (DVS) based system for reasoning about high speed motion.
Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection
This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs).
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error
We implement the objective with an adversarial Q-learning method in which Q and Qx are the action-value functions for extrinsic and secondary rewards, respectively.
Reward Prediction Error as an Exploration Objective in Deep RL
We then propose a deep reinforcement learning method, QXplore, which exploits the temporal difference error of a Q-function to solve hard exploration tasks in high-dimensional MDPs.
