Towards Generalizable Robotic Policies via Data-Driven Learning at Scale

Abstract

To enable robots to operate seamlessly in complex, real-world environments, they must master fine-grained manipulation skills and exhibit robust, adaptive behavior across diverse environments. This thesis explores a data-driven approach to learning generalizable and reactive manipulation policies by leveraging efficient data generation pipelines and expressive neural models. We first introduce BiDex, a low-cost teleoperation system for collecting high-quality demonstrations on dexterous bimanual tasks, addressing the challenge of acquiring real-world data. To overcome the limitations of scale and diversity in physical data collection, we present Neural MP, a simulation-based framework for autonomous data generation. Building on this foundation, we propose DRP, a learning paradigm that augments offline imitation learning with online fine-tuning and reactive components, enabling policies to perform reliably in dynamic and partially observable settings. Together, these contributions provide a practical recipe for developing robust robotic manipulation systems at scale.