Murphy Priosin(✱,✱)(@MPriosin71748):After injecting user data as a prior, we can train a new expert in ~20 minutes. No complicated reward engineering. No expensive reward-model fine-tuning. Just sparse, simple rewards are enough to adapt the policy across tasks. A scalable way to produce large numbers of high-quality robot experts with very low compute and human cost.

Murphy Priosin(✱,✱)

@MPriosin71748

Core contributor @axisrobotics Leading simulation + robotics + AI. @berkeley_ai

가입 December 2025

128 팔로잉 중 248 팬

Murphy Priosin(✱,✱)@MPriosin71748

2026.09.29 09:01

After injecting user data as a prior, we can train a new expert in ~20 minutes. No complicated reward engineering. No expensive reward-model fine-tuning. Just sparse, simple rewards are enough to adapt the policy across tasks. A scalable way to produce large numbers of high-quality robot experts with very low compute and human cost.