9 papers
ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models
Changyeon Kim, Kimin Lee, Yashraj Narang +1
Reinforcement learning (RL) post-training improves robot foundation models (RFMs) beyond their demonstrations, but real-world deployment demands more: learning new tasks efficientl…
RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
Byeongguk Jeon, Seonghyeon Ye, JaeHyeok Doo +4
Video world models are emerging as a scalable alternative for evaluating generalist robot policies, bypassing the physical constraints and engineering burdens of real-world deploym…
Beyond Monotonic Progress: Retry-Supervised Value Learning for Robot Imitation
Xinyao Qin, Junjie Lu, Kaixin Wang +7
Human demonstrations for robot imitation learning often contain mistakes and corrective behaviors, such as imprecise grasps, object misalignment, unstable contact, and repeated att…
HABIT: Human-Aware Behavior and Interaction Training Dataset for Robot Manipulation
Jaehwi Song, Suchae Jeong, Byeongguk Jeon +4
Large-scale demonstration datasets have been central to recent progress in general-purpose robot policies. However, existing datasets are collected in human-absent settings, and po…
SPACE: Enabling Learning from Cross-Robot Data Toward Generalist Policies
Haeone Lee, Byeongguk Jeon, Suchae Jeong +2
In robot learning, scaling training datasets across diverse embodiments and environments has become a dominant paradigm for learning generalizable robot policies. These policies ar…
Q-Flow: Stable and Expressive Reinforcement Learning with Flow-Based Policy
JaeHyeok Doo, Byeongguk Jeon, Seonghyeon Ye +2
There is growing interest in utilizing flow-based models as decision-making policies in reinforcement learning due to their high expressive capacity. However, effectively leveragin…
WarmPrior: Straightening Flow-Matching Policies with Temporal Priors
Sinjae Kang, Chanyoung Kim, Kaixin Wang +2
Generative policies based on diffusion and flow matching have become a dominant paradigm for visuomotor robotic control. We show that replacing the standard Gaussian source distrib…
Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search
Dongryung Lee, Sejune Joo, Kimin Lee +1
The problem of relocating a set of objects to designated areas amidst movable obstacles can be framed as a Geometric Task and Motion Planning (G-TAMP) problem, a subclass of task a…
Latent Action Pretraining from Videos
Seonghyeon Ye, Joel Jang, Byeongguk Jeon +13
We introduce Latent Action Pretraining for general Action models (LAPA), an unsupervised method for pretraining Vision-Language-Action (VLA) models without ground-truth robot actio…