MOKA: Open-Vocabulary Robotic Manipulation through Mark-Based Visual Prompting
(2403.03174)Abstract
Open-vocabulary generalization requires robotic systems to perform tasks involving complex and diverse environments and task goals. While the recent advances in vision language models (VLMs) present unprecedented opportunities to solve unseen problems, how to utilize their emergent capabilities to control robots in the physical world remains an open question. In this paper, we present MOKA (Marking Open-vocabulary Keypoint Affordances), an approach that employs VLMs to solve robotic manipulation tasks specified by free-form language descriptions. At the heart of our approach is a compact point-based representation of affordance and motion that bridges the VLM's predictions on RGB images and the robot's motions in the physical world. By prompting a VLM pre-trained on Internet-scale data, our approach predicts the affordances and generates the corresponding motions by leveraging the concept understanding and commonsense knowledge from broad sources. To scaffold the VLM's reasoning in zero-shot, we propose a visual prompting technique that annotates marks on the images, converting the prediction of keypoints and waypoints into a series of visual question answering problems that are feasible for the VLM to solve. Using the robot experiences collected in this way, we further investigate ways to bootstrap the performance through in-context learning and policy distillation. We evaluate and analyze MOKA's performance on a variety of manipulation tasks specified by free-form language descriptions, such as tool use, deformable body manipulation, and object rearrangement.
Overview
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Introduces MOKA, a method leveraging Vision-Language Models (VLMs) for robotic manipulation tasks through mark-based visual prompting, enabling open-vocabulary generalization.
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Describes a novel strategy for translating natural language task descriptions into visual prompts for VLMs, facilitating zero-shot generalization to new tasks.
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Evaluates MOKA's performance across diverse tasks, demonstrating its robustness and ability to improve with in-context learning or policy distillation.
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Highlights the potential of integrating VLMs with robotics and suggests future directions for exploring more complex tasks and improving visual prompting techniques.
MOKA: Bridging Vision-Language Models and Robotic Manipulation through Mark-Based Visual Prompting
Overview
The utilization of Vision-Language Models (VLMs) in robotic manipulation tasks presents a compelling opportunity to address the challenge of open-vocabulary generalization. The incorporation of these models into robotics could drastically extend the capability of robots to perform a wide array of tasks instructed through simple, free-form language. This paper introduces an innovative approach, Marking Open-vocabulary Keypoint Affordances (MOKA), which leverages pre-trained VLMs to predict affordances and generate corresponding motions for a robot to execute tasks described in natural language.
Methodology
MOKA embodies a novel strategy that aligns the predictions of VLMs with robotic actions through a point-based affordance representation, encapsulated in a compact, interpretable form. This methodology facilitates zero-shot generalization to new tasks by prompting the VLM with free-form language descriptions and annotated marks on RGB images, effectively transforming task specifications into visual question-answering challenges the VLM can address.
Hierarchical Prompting Strategy
The framework employs a hierarchical approach enabling high-level task decomposition followed by detailed low-level affordance reasoning. At the high level, the model dissects a task into feasible sub-tasks based on initial observations and language descriptions. Subsequently, for each sub-task, it predicts a set of keypoints and waypoints pertinent for motion execution, adhering to a structured affordance representation defined by the authors.
Mark-Based Visual Prompting
A crucial component of MOKA is its mark-based visual prompting technique, which annotates visual marks on image observations to guide the VLM towards useful visual cues for affordance reasoning. This technique shifts the challenge from direct prediction of continuous values to selecting among multiple choices, significantly aligning with VLMs’ strengths.
Evaluation and Results
MOKA was assessed across various manipulation tasks involving tool use, object rearrangement, and interaction with deformable bodies, showcasing robust performance across different instructions, object arrangements, and task environments. The approach demonstrates remarkable capability in zero-shot settings and shows further improvement when using in-context learning or policy distillation from collected task successes.
Implications and Future Directions
This research underscores the potential of leveraging VLMs for robotic manipulation, paving the path for future explorations in this area. The success of MOKA suggests a scalable strategy for extending robotic capabilities to a broader spectrum of tasks without the need for extensive task-specific programming or training. Furthermore, the ability of MOKA to generate data for policy distillation indicates a promising direction for amalgamating model-based and learning-based approaches in robotics.
Theoretical and Practical Contributions
- Introduces a point-based affordance representation that effectively translates VLM predictions into robotic actions.
- Proposes a mark-based visual prompting method, enhancing VLM’s applicability to robotic manipulation tasks, especially in an open-vocabulary context.
- Demonstrates the utility of pre-trained VLMs in solving diverse manipulation tasks specified by free-form language, achieving state-of-the-art performance.
Future Work
While MOKA marks a significant step forward, the exploration of more complex manipulation tasks, including bimanual coordination and tasks requiring delicate force control, remains open. Further development of VLMs and advancements in visual prompting strategies are critical for bridging remaining gaps between language understanding and physical interaction in robotics.
Conclusion
MOKA offers a promising approach towards enabling robots to understand and execute a wide range of manipulation tasks conveyed through natural language, leveraging the vast knowledge encapsulated in VLMs. This work not only presents a methodological advancement in robotic manipulation but also provides insight into the potential synergies between the fields of natural language processing, computer vision, and robotics.