Author

Byron Boots

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#machine learning Preprint Oct 2026

A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control

General-purpose robots must perform a wide range of tasks from agile locomotion to dexterous manipulation. While sim-to-real reinforcement learning (RL) has proven to be a useful tool for this goal, current RL pipelines depend on engineering-heavy, per-task structural priors such as shaped rewards and demonstrations. R...

Octi Zhang, Mateo Guaman Castro, Patrick Yin et al. · 0 citations

Preprint Jul 2026

Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation

This work constraining reward function candidacy during IRL to the space of CBFs yields a formulation that exhibits safe online control with continuous experiential improvement, and demonstrates that the recovered barrier function is robust to unsafe states entirely absent from the expert data.

Anubhav Vishwakarma, Bhaumik Mehta, Caleb Hsu et al. · 0 citations