Publikation
Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
Zechu Li; Yufeng Jin; Puze Liu; Jan Peters; Georgia Chalvatzaki
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2606.22471, Pages 1-8, arXiv, 2026.
Zusammenfassung
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 demonstra-
tions by overcoming challenges such as morphology mismatch,
missing physical interactions, and the generation of robot
actions. However, existing approaches based on human teleop-
eration 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 annota-
tions. 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.