10 papers
Contrastive Representation Regularization for Vision-Language-Action Models
Taeyoung Kim, Jimin Lee, Myungkyu Koo +5
Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained Vision-Language Models (VLMs). However,…
RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models
Dongyoung Kim, Sumin Park, Woomin Song +6
Improving embodied reasoning in multimodal-large-language models (MLLMs) is essential for building vision-language-action models (VLAs) on top of them to readily translate multimod…
RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains
Yuanhang Zhang, Younggyo Seo, Juyue Chen +7
Humanoid perceptive locomotion has made significant progress and shows great promise, yet achieving robust multi-directional locomotion on complex terrains remains underexplored. T…
Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics
Dongyoung Kim, Sumin Park, Huiwon Jang +3
Large Vision-Language Models (LVLMs) have recently shown great promise in advancing robotics by combining embodied reasoning with robot control. A common approach involves training…
Learning Sim-to-Real Humanoid Locomotion in 15 Minutes
Younggyo Seo, Carmelo Sferrazza, Juyue Chen +3
Massively parallel simulation has reduced reinforcement learning (RL) training time for robots from days to minutes. However, achieving fast and reliable sim-to-real RL for humanoi…
Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Reinforcement Learning
Younggyo Seo, Pieter Abbeel
Predicting a sequence of actions has been crucial in the success of recent behavior cloning algorithms in robotics. Can similar ideas improve reinforcement learning (RL)? We answer…