4 papers
TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning
Matthew M. Hong, Jesse Zhang, Anusha Nagabandi +1
Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narro…
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…
Multi-Agent Path Finding via Offline RL and LLM Collaboration
Merve Atasever, Matthew Hong, Mihir Nitin Kulkarni +2
Multi-Agent Path Finding (MAPF) poses a significant and challenging problem critical for applications in robotics and logistics, particularly due to its combinatorial complexity an…
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
Anthony Liang, Pavel Czempin, Matthew Hong +5
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…