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Found 9 papers, 1 papers with code
FourCastNeXt: Optimizing FourCastNet Training for Limited Compute
FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet computational requirements.
PcLast: Discovering Plannable Continuous Latent States
Goal-conditioned planning benefits from learned low-dimensional representations of rich, high-dimensional observations.
See to Touch: Learning Tactile Dexterity through Visual Incentives
First, we use a contrastive-based objective to learn visual representations.
Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play
In the first phase, we collect 2. 5 hours of play data, which is used to train self-supervised tactile encoders.
Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation
Optimizing behaviors for dexterous manipulation has been a longstanding challenge in robotics, with a variety of methods from model-based control to model-free reinforcement learning having been previously explored in literature.
Context is Everything: Implicit Identification for Dynamics Adaptation
Understanding environment dynamics is necessary for robots to act safely and optimally in the world.
BAM: Bayes with Adaptive Memory
Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs.
BAM: Bayes Augmented with Memory
Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs.
It's the Journey Not the Destination: Building Genetic Algorithms Practitioners Can Trust
The results from a survey showing the attitudes of engineers and students with design experience with respect to optimisation algorithms are presented.
