Search Results for author:
Found 14 papers, 1 papers with code
Task planning and explanation with virtual actions
This paper shows how to introduce virtual action to extend action models to make the graph to be connected: i) explicitly defines static predicate (type, permanent properties, etc) or dynamic predicate (state); ii) constructs a full virtual action or a semi-virtual action for each state; iii) finds the cause of the planning failure through a progressive planning approach.
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control
Our meta policy tries to manipulate the next optimal state and actual action is produced by the inverse dynamics model.
Learning and Reasoning for Robot Dialog and Navigation Tasks
More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.
Adaptive Dialog Policy Learning with Hindsight and User Modeling
Reinforcement learning methods have been used to compute dialog policies from language-based interaction experiences.
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning
First, we develop an algorithm, called Experience Grafting (EG), to enable RL agents to reorganize segments of the few high-quality trajectories from the experience pool to generate many synthetic trajectories while retaining the quality.
Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking
These 1D heatmaps reduce spatial complexity significantly compared to current heatmap regression methods, which use 2D heatmaps to represent the joint distributions of x and y coordinates.
Robot Representation and Reasoning with Knowledge from Reinforcement Learning
In this work, we integrate logical-probabilistic KRR with model-based RL, enabling agents to simultaneously reason with declarative knowledge and learn from interaction experiences.
KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning.
Goal-oriented Dialogue Policy Learning from Failures
Reinforcement learning methods have been used for learning dialogue policies.
Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection
The proposed training strategy uses the anchors with IoUs between the first and second threshold, which can consistently improve the performance of face detection.
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching
Depth scans acquired from different views may contain nuisances such as noise, occlusion, and varying point density.
Understanding User Instructions by Utilizing Open Knowledge for Service Robots
Understanding user instructions in natural language is an active research topic in AI and robotics.
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search
Monte-Carlo tree search is drawing great interest in the domain of planning under uncertainty, particularly when little or no domain knowledge is available.
