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Found 12 papers, 3 papers with code
Flow-based Domain Randomization for Learning and Sequencing Robotic Skills
Domain randomization in reinforcement learning is an established technique for increasing the robustness of control policies trained in simulation.
Semi-Supervised Neural Processes for Articulated Object Interactions
The scarcity of labeled action data poses a considerable challenge for developing machine learning algorithms for robotic object manipulation.
Structured Latent Variable Models for Articulated Object Interaction
In this paper, we investigate a scenario in which a robot learns a low-dimensional representation of a door given a video of the door opening or closing.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems
The rapid integration of artificial intelligence across traditional research domains has generated an amalgamation of nomenclature.
Active Learning of Abstract Plan Feasibility
Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences, and when a plan is found, lower level motion plans are generated.
Visual Prediction of Priors for Articulated Object Interaction
Given a novel object, the objective is to maximize reward with few interactions.
Leveraging Past References for Robust Language Grounding
We therefore reframe the grounding problem from the perspective of coreference detection and propose a neural network that detects when two expressions are referring to the same object.
The RLLChatbot: a solution to the ConvAI challenge
This article presents in detail the RLLChatbot that participated in the 2017 ConvAI challenge.
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem.
Predicting Success in Goal-Driven Human-Human Dialogues
We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition.
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available.
