Title: Versatile and Generalizable Manipulation via Goal-Conditioned Reinforcement Learning with Grounded Object Detection Show Chinese title

Title: 通过基于目标的强化学习与基础物体检测的多功能和可泛化操作

Abstract: General-purpose robotic manipulation, including reach and grasp, is essential for deployment into households and workspaces involving diverse and evolving tasks. Recent advances propose using large pre-trained models, such as Large Language Models and object detectors, to boost robotic perception in reinforcement learning. These models, trained on large datasets via self-supervised learning, can process text prompts and identify diverse objects in scenes, an invaluable skill in RL where learning object interaction is resource-intensive. This study demonstrates how to integrate such models into Goal-Conditioned Reinforcement Learning to enable general and versatile robotic reach and grasp capabilities. We use a pre-trained object detection model to enable the agent to identify the object from a text prompt and generate a mask for goal conditioning. Mask-based goal conditioning provides object-agnostic cues, improving feature sharing and generalization. The effectiveness of the proposed framework is demonstrated in a simulated reach-and-grasp task, where the mask-based goal conditioning consistently maintains a $\sim$90\% success rate in grasping both in and out-of-distribution objects, while also ensuring faster convergence to higher returns. Show Chinese abstract

Abstract: 通用的机器人操作，包括到达和抓取，在涉及多样性和不断变化的任务的家庭和工作空间部署中是必不可少的。 最近的进展提出了使用大型预训练模型，如大型语言模型和物体检测器，以在强化学习中提升机器人感知能力。 这些模型通过自监督学习在大规模数据集上进行训练，可以处理文本提示并在场景中识别各种物体，这是强化学习中一种宝贵的技能，因为在强化学习中学习物体交互是资源密集型的。 本研究展示了如何将此类模型集成到目标条件强化学习中，以实现通用且灵活的机器人到达和抓取能力。 我们使用预训练的物体检测模型使代理能够从文本提示中识别物体并生成用于目标条件的掩码。 基于掩码的目标条件提供了与物体无关的提示，提高了特征共享和泛化能力。 所提出的框架的有效性在模拟的到达和抓取任务中得到验证，其中基于掩码的目标条件在抓取分布内外的物体时始终保持$\sim$90% 的成功率，同时确保更快地收敛到更高的回报。