6 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…
Robust Skills, Brittle Grounding: Diagnosing Restricted Generalization in Vision-Language Action Policies via Multi-Object Picking
David Emukpere, Romain Deffayet, Jean-Michel Renders
Vision-language action (VLA) policies often report strong manipulation benchmark performance with relatively few demonstrations, but it remains unclear whether this reflects robust…
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression
Sami Jullien, Romain Deffayet, Jean-Michel Renders +2
Distributional reinforcement learning (RL) has proven useful in multiple benchmarks as it enables approximating the full distribution of returns and extracts rich feedback from env…
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…
An Offline Metric for the Debiasedness of Click Models
Romain Deffayet, Philipp Hager, Jean-Michel Renders +1
A well-known problem when learning from user clicks are inherent biases prevalent in the data, such as position or trust bias. Click models are a common method for extracting infor…