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Found 3 papers, 2 papers with code
Efficient and Interpretable Robot Manipulation with Graph Neural Networks
Manipulation tasks, like loading a dishwasher, can be seen as a sequence of spatial constraints and relationships between different objects.
Learning State-Dependent Losses for Inverse Dynamics Learning
In both settings, the structured and state-dependent learned losses improve online adaptation speed, when compared to standard, state-independent loss functions.
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm
The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots.
Robotics
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