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MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting (2403.03174v3)

Published 5 Mar 2024 in cs.RO and cs.AI

Abstract: Open-world generalization requires robotic systems to have a profound understanding of the physical world and the user command to solve diverse and complex tasks. While the recent advancement in vision-LLMs (VLMs) has offered unprecedented opportunities to solve open-world problems, how to leverage their capabilities to control robots remains a grand challenge. In this paper, we introduce Marking Open-world Keypoint Affordances (MOKA), an approach that employs VLMs to solve robotic manipulation tasks specified by free-form language instructions. Central to our approach is a compact point-based representation of affordance, which bridges the VLM's predictions on observed images and the robot's actions in the physical world. By prompting the pre-trained VLM, our approach utilizes the VLM's commonsense knowledge and concept understanding acquired from broad data sources to predict affordances and generate motions. To facilitate the VLM's reasoning in zero-shot and few-shot manners, we propose a visual prompting technique that annotates marks on images, converting affordance reasoning into a series of visual question-answering problems that are solvable by the VLM. We further explore methods to enhance performance with robot experiences collected by MOKA through in-context learning and policy distillation. We evaluate and analyze MOKA's performance on various table-top manipulation tasks including tool use, deformable body manipulation, and object rearrangement.

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Citations (23)
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Summary

  • The paper introduces a point-based affordance representation that leverages pre-trained VLMs to translate natural language into precise robotic actions.
  • It employs a hierarchical prompting strategy with visual marks to decompose tasks into sub-tasks, enabling zero-shot generalization.
  • Evaluations across diverse tasks, including tool use and object manipulation, highlight robust performance and potential for scalable robotic applications.

MOKA: Bridging Vision-LLMs and Robotic Manipulation through Mark-Based Visual Prompting

Overview

The utilization of Vision-LLMs (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.