# Versatile and Generalizable Manipulation via Goal-Conditioned Reinforcement Learning with Grounded Object Detection Source: [Versatile and Generalizable Manipulation via Goal-Conditioned Reinforcement Learning with Grounded Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:57:50.280Z Authors: Huiyi Wang, Fahim Shahriar, Alireza Azimi, Gautham Vasan, Rupam Mahmood, Colin Bellinger Published: 2025-07-14T21:21:46.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()