3 accepted papers
Discrete latent bottlenecks in variational autoencoders (VAEs) offer high bit efficiency and can be modeled with autoregressive discrete distributions, enabling parameter-efficient multimodal search with transformers. However, discrete random variables do not allow for exact differentiable parameter…
Sample efficiency is a crucial problem in deep reinforcement learning. Recent algorithms, such as REDQ and DroQ, found a way to improve the sample efficiency by increasing the update-to-data (UTD) ratio to 20 gradient update steps on the critic per environment sample. However, this comes at the expe…
We present in-hand manipulation skills on a dexterous; compliant; anthropomorphic hand. Even though these skills were derived in a simplistic manner; they exhibit surprising robustness to variations in shape; size; weight; and placement of the manipulated object. They are also very insensitive to va…