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Found 9 papers, 1 papers with code
Vision based Virtual Guidance for Navigation
This paper explores the impact of virtual guidance on mid-level representation-based navigation, where an agent performs navigation tasks based solely on visual observations.
Pixel-Wise Prediction based Visual Odometry via Uncertainty Estimation
This paper introduces pixel-wise prediction based visual odometry (PWVO), which is a dense prediction task that evaluates the values of translation and rotation for every pixel in its input observations.
Investigation of Factorized Optical Flows as Mid-Level Representations
In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks.
Mixture of Step Returns in Bootstrapped DQN
Nevertheless, integrating step returns into a single target sacrifices the diversity of the advantages offered by different step return targets.
Exploration via Flow-Based Intrinsic Rewards
Exploration bonuses derived from the novelty of observations in an environment have become a popular approach to motivate exploration for reinforcement learning (RL) agents in the past few years.
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
We propose to employ optical flow estimation errors to examine the novelty of new observations, such that agents are able to memorize and understand the visited states in a more comprehensive fashion.
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information
The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver.
Dynamic Video Segmentation Network
We explore the use of a decision network to adaptively assign different frame regions to different networks based on a metric called expected confidence score.
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation
Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of simulators as the training platform.
