RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning

• URL: http://arxiv.org/abs/2605.30957v1
• Date: Fri, 29 May 2026 07:53:19 GMT
• Title: RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning
• Abstract summary: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robot control.<n>These models are constrained by the availability of high-quality robot trajectory data.<n>We propose RDGen, a sim-to-real reinforcement learning framework for generating high-quality robot demonstrations.
• Score: 6.009885909139946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robot control. However, their performance remains fundamentally constrained by the availability of high-quality robot trajectory data. In current robot learning practice, such data are primarily collected through human teleoperation, which is labor-intensive, costly, and difficult to scale. In this paper, we propose RDGen, a sim-to-real reinforcement learning framework for generating high-quality robot demonstrations. Rather than employing reinforcement learning solely as the final control policy, RDGen leverages trained RL policies as a structured trajectory generator. The system consists of a VLM-based task parser that identifies task-relevant objects, a Grounding DINO-based object localizer, and an RL policy transferred from simulation to the real robot. Successful rollouts are then harvested as clean, high-quality demonstrations for downstream VLA training, while the simulation stage further provides a scalable source of additional trajectories at little marginal cost. Experiments on a pick-and-place task demonstrate that the transferred RL policy achieves a high task success rate. Compared with human teleoperation, RDGen produces significantly smoother trajectories and yields superior downstream VLA performance. These results indicate that RL-generated demonstrations can serve as more reliable and consistent supervisory signals for robot policy learning.

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