Matthew M. Hong — accepted AI-conference papers

2026

HAND Me the Data: Fast Robot Adaptation Via Hand Path Retrieval

Matthew M Hong, Anthony Liang, Kevin Kim, Harshitha Belagavi Rajaprakash, Jesse Thomason, Erdem Bıyık +1

ICRA 2026poster

We present HAND, a simple and time-efficient method for teaching robots new manipulation tasks through human hand demonstrations. Instead of relying on task-specific robot demonstrations collected via teleoperation, HAND uses easy-to-provide hand demonstrations to retrieve relevant behaviors from ta…

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2026

TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning

Matthew M. Hong, Jesse Zhang, Anusha Nagabandi, Abhishek Gupta

RSS 2026poster

Efficient exploration remains a bottleneck in reinforcement learning (RL), particularly for long-horizon, high-dimensional tasks. While recent methods leverage pre-trained policies for guidance, they are often constrained by the base policy’s original behavior distribution. We introduce Timestep Mod…

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