The dream of a general-purpose robot assisting us in everyday tasks has long haunted the robotics community. While recent advancements in data-driven approaches and large models have sparked optimism, current systems remain brittle and fail spectacularly when encountering unforeseen scenarios. This article explores the challenges hindering robust robot manipulation and proposes OK-Robot, an “Open-Knowledge Robot” framework leveraging state-of-the-art models to bridge the gap between vision and action.
The Crossroads of Vision and Robotics:
Large vision models have achieved impressive feats in semantic understanding, detection, and language-image connections. Conversely, robots boast mature navigation, grasping, and re-arrangement skills. Ironically, combining these powerful elements frequently leads to subpar performance. The recent NeurIPS 2023 challenge for open-vocabulary mobile manipulation (OVMM) further exemplifies this struggle, with the winning solution achieving a mere 33% success rate.
Why Open-Vocabulary Robotics is Hard:
The difficulty of open-vocabulary robotics stems not from a single hurdle but a cascade of issues. Inaccuracies in each component multiply, leading to overall failure. For instance, poor object retrieval in homes depends on query quality. Navigation targets gleaned from vision-language models (VLMs) might be unreachable for the robot. Furthermore, diverse grasping models exhibit stark performance differences. Tackling this problem requires a flexible framework that seamlessly integrates VLMs and robotic primitives while accommodating future advancements in both fields.
Introducing OK-Robot:
OK-Robot, an Open Knowledge Robot, addresses this challenge by fusing cutting-edge VLMs with powerful robotic navigation and grasping primitives to enable pick-and-drop tasks. “Open knowledge” refers to models trained on vast, publicly available datasets. Upon entering a new home environment, OK-Robot ingests a scan acquired from an iPhone. Dense vision-language representations are then computed using LangSam and CLIP and stored in a semantic memory. Given a natural language query for an object, its language representation is matched with the memory. Subsequently, navigation and grasping primitives are sequentially applied to locate and pick up the object (and similarly for dropping).
Real-World Evaluation:
The researchers evaluated OK-Robot in 10 real-world homes, achieving an average 58.5% success rate in zero-shot deployments. Notably, this success hinges on the environment’s “naturalness.” They observed that improved queries, decluttered spaces, and excluding adversarial objects (e.g., too large, slippery) pushed the success rate to 82.4%. Our key findings are:
- VLMs shine in open-vocabulary navigation: Pre-trained VLMs like CLIP and OWL-ViT excel at identifying arbitrary objects and enabling zero-shot navigation towards them.
- Direct application of pre-trained grasping models: Similar to VLMs, robots pre-trained on extensive data can be directly applied to open-vocabulary grasping in homes without additional training or fine-tuning.
- Combination reigns supreme: Pre-trained models can be effectively combined with no training using a simple state-machine model. Moreover, employing heuristics to address the robot’s physical limitations yields better real-world success rates.
- Challenges remain: While surpassing prior work, OK-Robot’s performance can be further enhanced by improvements in VLMs, robot models, and robot morphology.
Conclusion:
OK-Robot demonstrates the potential of open-knowledge robots by utilizing pre-trained vision and manipulation models. Further research focusing on refining these models and tackling physical limitations promises to bring us closer to the dream of general-purpose robots seamlessly interacting with our complex and ever-changing environments.
For more information, read this research paper on OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics.
