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Found 6 papers, 1 papers with code
Automated Discovery of Functional Actual Causes in Complex Environments
This paper introduces functional actual cause (FAC), a framework that uses context-specific independencies in the environment to restrict the set of actual causes.
Learning Action-based Representations Using Invariance
Robust reinforcement learning agents using high-dimensional observations must be able to identify relevant state features amidst many exogeneous distractors.
Granger-Causal Hierarchical Skill Discovery
Reinforcement Learning (RL) has demonstrated promising results in learning policies for complex tasks, but it often suffers from low sample efficiency and limited transferability.
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory
Robots in human environments will need to interact with a wide variety of articulated objects such as cabinets, drawers, and dishwashers while assisting humans in performing day-to-day tasks.
Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning
Deep reinforcement learning (DRL) is capable of learning high-performing policies on a variety of complex high-dimensional tasks, ranging from video games to robotic manipulation.
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations
Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error.
