4 citations · 5 across the 3 of their papers we have counts for
4 papers · 1 filter
Flow Policy Gradients for Robot Control
Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh +9
Likelihood-based policy gradient methods are the dominant approach for training robot control policies from rewards. These methods rely on differentiable action likelihoods, which…
FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control
Younggyo Seo, Carmelo Sferrazza, Haoran Geng +3
Reinforcement learning (RL) has driven significant progress in robotics, but its complexity and long training times remain major bottlenecks. In this report, we introduce FastTD3,…
Body Transformer: Leveraging Robot Embodiment for Policy Learning
Carmelo Sferrazza, Dun-Ming Huang, Fangchen Liu +2
In recent years, the transformer architecture has become the de facto standard for machine learning algorithms applied to natural language processing and computer vision. Despite n…
The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning
Carmelo Sferrazza, Younggyo Seo, Hao Liu +2
Humans rely on the synergy of their senses for most essential tasks. For tasks requiring object manipulation, we seamlessly and effectively exploit the complementarity of our sense…