Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation

2026-06-21

AI Summary

Training general-purpose bimanual dexterous manipulation policies is hindered by the scarcity of high-quality, diverse data, as existing synthetic approaches struggle to simultaneously ensure task diversity and robotic feasibility. This work proposes the first reinforcement learning data generation framework that integrates a universal reward mechanism, domain randomization, and language-conditioned annotations to systematically produce diverse yet executable bimanual manipulation data. The method substantially enhances the generalization performance of language-guided multitask policies across three representative dexterous manipulation tasks, effectively overcoming the longstanding trade-off between diversity and feasibility that plagues conventional data generation paradigms.

Abstract

A key bottleneck in training generalist policies for bimanual dexterous manipulation is the lack of large-scale, high-quality datasets. Synthetic data generation in simulation provides a scalable alternative to human video demonstrations by overcoming challenges such as morphology mismatch, missing physical interactions, and the generation of robot actions. However, existing approaches based on human teleoperation offer limited task diversity, as object-centric trajectory matching often neglects the feasibility of robot execution. Reinforcement learning (RL) enables broader scalability but is often constrained by handcrafted, task-specific rewards. In this work, we propose a systematic RL-based data generation pipeline that integrates generalizable reward design, effective domain randomization, and language-conditioned task annotations. This pipeline synthesizes diverse, high-quality datasets for dexterous bimanual manipulation and enables training of language-conditioned multi-task policies. Our experiments show that the generated data significantly improves generalization across three representative manipulation tasks.

Problem

• bimanual dexterous manipulation
• scalable data generation
• task diversity
• robot execution feasibility
• language-conditioned policies

Innovation

• reinforcement learning
• language-conditioned manipulation
• bimanual dexterous manipulation
• scalable data generation
• generalizable reward design