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Found 11 papers, 6 papers with code
Speech Emotion Recognition Via CNN-Transformer and Multidimensional Attention Mechanism
In this paper, to model local and global information at different levels of granularity in speech and capture temporal, spatial and channel dependencies in speech signals, we propose a Speech Emotion Recognition network based on CNN-Transformer and multi-dimensional attention mechanisms.
Masked Trajectory Models for Prediction, Representation, and Control
We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making.
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Contrary to inferences from prior work, we find that scaling dataset size and diversity does not improve performance universally (but does so on average).
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
We identify key ingredients for leveraging demonstrations in model learning -- policy pretraining, targeted exploration, and oversampling of demonstration data -- which forms the three phases of our model-based RL framework.
Deep Reinforcement Learning
Model-based Reinforcement Learning
+2
Transformers are Adaptable Task Planners
Every home is different, and every person likes things done in their particular way.
Differentiable and Learnable Robot Models
Building differentiable simulations of physical processes has recently received an increasing amount of attention.
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
In this paper, we explore how we can endow robots with the ability to learn correspondences between their own skills, and those of morphologically different robots in different domains, in an entirely unsupervised manner.
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
Curious iLQR: Resolving Uncertainty in Model-based RL
In this work, we propose a model-based reinforcement learning (MBRL) framework that combines Bayesian modeling of the system dynamics with curious iLQR, an iterative LQR approach that considers model uncertainty.
Model-based Reinforcement Learning
reinforcement-learning
+2
