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Found 9 papers, 1 papers with code
FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection
Our model consists of a hybrid network of convolution and transformer blocks to learn per-AU features and to model AU co-occurrences.
Learning from Demonstration with Weakly Supervised Disentanglement
We show that such alignment is best achieved through the use of labels from the end user, in an appropriately restricted vocabulary, in contrast to the conventional approach of the designer picking a prior over the latent variables.
Disentangled Relational Representations for Explaining and Learning from Demonstration
Learning from demonstration is an effective method for human users to instruct desired robot behaviour.
Composing Diverse Policies for Temporally Extended Tasks
Robot control policies for temporally extended and sequenced tasks are often characterized by discontinuous switches between different local dynamics.
Hybrid system identification using switching density networks
This paper introduces switching density networks, which rely on a categorical reparametrisation for hybrid system identification.
DynoPlan: Combining Motion Planning and Deep Neural Network based Controllers for Safe HRL
Many realistic robotics tasks are best solved compositionally, through control architectures that sequentially invoke primitives and achieve error correction through the use of loops and conditionals taking the system back to alternative earlier states.
Robotics
Using Causal Analysis to Learn Specifications from Task Demonstrations
In this work we show that it is possible to learn a generative model for distinct user behavioral types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space.
Interpretable Latent Spaces for Learning from Demonstration
Effective human-robot interaction, such as in robot learning from human demonstration, requires the learning agent to be able to ground abstract concepts (such as those contained within instructions) in a corresponding high-dimensional sensory input stream from the world.
Grounding Symbols in Multi-Modal Instructions
As robots begin to cohabit with humans in semi-structured environments, the need arises to understand instructions involving rich variability---for instance, learning to ground symbols in the physical world.
