OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics (2401.12202v2)

Abstract: Remarkable progress has been made in recent years in the fields of vision, language, and robotics. We now have vision models capable of recognizing objects based on language queries, navigation systems that can effectively control mobile systems, and grasping models that can handle a wide range of objects. Despite these advancements, general-purpose applications of robotics still lag behind, even though they rely on these fundamental capabilities of recognition, navigation, and grasping. In this paper, we adopt a systems-first approach to develop a new Open Knowledge-based robotics framework called OK-Robot. By combining Vision-LLMs (VLMs) for object detection, navigation primitives for movement, and grasping primitives for object manipulation, OK-Robot offers a integrated solution for pick-and-drop operations without requiring any training. To evaluate its performance, we run OK-Robot in 10 real-world home environments. The results demonstrate that OK-Robot achieves a 58.5% success rate in open-ended pick-and-drop tasks, representing a new state-of-the-art in Open Vocabulary Mobile Manipulation (OVMM) with nearly 1.8x the performance of prior work. On cleaner, uncluttered environments, OK-Robot's performance increases to 82%. However, the most important insight gained from OK-Robot is the critical role of nuanced details when combining Open Knowledge systems like VLMs with robotic modules. Videos of our experiments and code are available on our website: https://ok-robot.github.io

Synopsis

In the paper "OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics," by Liu et al., a novel framework is presented that integrates state-of-the-art Vision-LLMs (VLMs) with robust robotic primitives to perform pick-and-drop tasks in home environments without requiring task-specific training. The framework, named OK-Robot, utilizes Open Knowledge — models trained on large, publicly available datasets — to understand and manipulate objects based on natural language queries. The paper demonstrates both the feasibility and the challenges of deploying such a system in a real-world setting.

System Overview

OK-Robot is composed of three main modules: open-vocabulary object navigation, RGB-D grasping, and a dropping heuristic. The navigation module employs a semantic memory that encodes visual-language representations to localize objects in response to verbal commands. For grasping, the system uses AnyGrasp, a pretrained model that generates grasp poses, which are filtered according to the target object's semantic segmentation leveraging LangSam. Dropping heuristics then determine suitable placement locations. These subsystems are executed sequentially through a state-machine model induced by the user's command.

Performance Evaluation

The evaluation in real-world domestic environments underscores the accomplishments and limitations of OK-Robot. Across 10 homes, the system achieved a 58.5% success rate for pick-and-drop tasks in cluttered settings, which improved to 82.4% in cleaner environments, thus setting a new state-of-the-art in Open Vocabulary Mobile Manipulation (OVMM). The experiments further illustrate that performance is highly sensitive to environmental factors such as clutter and object accessibility.

Challenges and Insights

An analysis of performance sheds light on pivotal aspects for future research, such as improving semantic queries for object retrieval, developing grasp planning mechanisms, enhancing user interactions to resolve query ambiguities, and improving error recovery strategies. While hardware constraints such as payload capacity and reach limit the scope of object manipulation, these issues point to broader systemic challenges in employing open-knowledge models for robotic tasks.

Overall, the work presents an encouraging direction for robotics, emphasizing the importance of nuanced integration between vision-language understanding and physical manipulation while highlighting the need for further innovations in model integration, interactive systems, and robust hardware design to fully realize the potential of autonomous robots in unstructured human environments.