3 papers
cs.LG2025
Plantain: Plan-Answer Interleaved Reasoning
Anthony Liang, Jonathan Berant, Adam Fisch +3
Reasoning models often spend a significant amount of time thinking before they generate a visible response. In the meantime, they do not give the user any hints as to whether their…
cs.RO2025
HAND Me the Data: Fast Robot Adaptation via Hand Path Retrieval
Matthew Hong, Anthony Liang, Kevin Kim +4
We hand the community HAND, a simple and time-efficient method for teaching robots new manipulation tasks through human hand demonstrations. Instead of relying on task-specific rob…
cs.RO2025
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
Anthony Liang, Pavel Czempin, Matthew M. Hong +4
Learning robot control policies from demonstrations typically requires action-labeled expert data, which is expensive to collect through teleoperation. We study a more practical se…