32 citations · 32 across the 4 of their papers we have counts for
4 papers
ProgVLA: Progress-Aware Robot Manipulation Skill Learning
Seungsu Kim, Jinyoung Choi, Seungmin Baek +1
We present ProgVLA, a compact vision-language-action (VLA) model designed for reliable robot manipulation under tight compute and memory budgets. The model specifically focuses on…
Behavioral Mode Discovery for Fine-tuning Multimodal Generative Policies
Alberta Longhini, David Emukpere, Jean-Michel Renders +1
We address the problem of fine-tuning pre-trained generative policies with reinforcement learning (RL) while preserving the multimodality of their action distributions. Existing me…
Disentangled Object-Centric Image Representation for Robotic Manipulation
David Emukpere, Romain Deffayet, Bingbing Wu +6
Learning robotic manipulation skills from vision is a promising approach for developing robotics applications that can generalize broadly to real-world scenarios. As such, many app…
From exploration to control: learning object manipulation skills through novelty search and local adaptation
Seungsu Kim, Alexandre Coninx, Stephane Doncieux
Programming a robot to deal with open-ended tasks remains a challenge, in particular if the robot has to manipulate objects. Launching, grasping, pushing or any other object intera…