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Found 7 papers, 1 papers with code
MO2: Model-Based Offline Options
The ability to discover useful behaviours from past experience and transfer them to new tasks is considered a core component of natural embodied intelligence.
Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning
Key to these fields is the use of information asymmetry across architectural modules to bias which skills are learnt.
Attention-Privileged Reinforcement Learning
Image-based Reinforcement Learning is known to suffer from poor sample efficiency and generalisation to unseen visuals such as distractors (task-independent aspects of the observation space).
Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis
In this paper we explore the richness of information captured by the latent space of a vision-based generative model.
Attention Privileged Reinforcement Learning for Domain Transfer
Applying reinforcement learning (RL) to physical systems presents notable challenges, given requirements regarding sample efficiency, safety, and physical constraints compared to simulated environments.
Imagine That! Leveraging Emergent Affordances for Tool Synthesis in Reaching Tasks
In this paper we investigate an artificial agent's ability to perform task-focused tool synthesis via imagination.
TACO: Learning Task Decomposition via Temporal Alignment for Control
Many advanced Learning from Demonstration (LfD) methods consider the decomposition of complex, real-world tasks into simpler sub-tasks.
