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Found 5 papers, 0 papers with code
Learning rewards for robotic ultrasound scanning using probabilistic temporal ranking
This work considers the inverse problem, where the goal of the task is unknown, and a reward function needs to be inferred from exploratory example demonstrations provided by a demonstrator, for use in a downstream informative path-planning policy.
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.
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.
