GeoManip: Geometric Constraints as General Interfaces for Robot Manipulation (2501.09783v1)

Abstract: We present GeoManip, a framework to enable generalist robots to leverage essential conditions derived from object and part relationships, as geometric constraints, for robot manipulation. For example, cutting the carrot requires adhering to a geometric constraint: the blade of the knife should be perpendicular to the carrot's direction. By interpreting these constraints through symbolic language representations and translating them into low-level actions, GeoManip bridges the gap between natural language and robotic execution, enabling greater generalizability across diverse even unseen tasks, objects, and scenarios. Unlike vision-language-action models that require extensive training, operates training-free by utilizing large foundational models: a constraint generation module that predicts stage-specific geometric constraints and a geometry parser that identifies object parts involved in these constraints. A solver then optimizes trajectories to satisfy inferred constraints from task descriptions and the scene. Furthermore, GeoManip learns in-context and provides five appealing human-robot interaction features: on-the-fly policy adaptation, learning from human demonstrations, learning from failure cases, long-horizon action planning, and efficient data collection for imitation learning. Extensive evaluations on both simulations and real-world scenarios demonstrate GeoManip's state-of-the-art performance, with superior out-of-distribution generalization while avoiding costly model training.

• The paper introduces a training-free framework that leverages geometric constraints to bridge natural language instructions with precise low-level robotic actions.
• It employs a novel three-component architecture—geometry parser, constraint generator, and trajectory solver—to extract and enforce task-specific geometric features.
• Extensive virtual and real-world evaluations demonstrate that GeoManip achieves robust out-of-distribution generalization and efficient manipulation across diverse scenarios.

Overview of GeoManip: Geometric Constraints as General Interfaces for Robot Manipulation

The paper "GeoManip: Geometric Constraints as General Interfaces for Robot Manipulation" presents a framework designed to enhance robotic manipulation by introducing a geometric constraint-based interface. This interface facilitates the mapping of high-level language descriptions into actionable, low-level robotic tasks. GeoManip leverages foundational models, integrating geometric data as constraints without the need for extensive training typically required by other vision-language-action models. This innovative approach allows for effective manipulation across a range of diverse, and even previously unseen, scenarios, tasks, and objects.

Key Contributions

GeoManip's primary contribution is its use of geometric constraints to bridge the gap between natural language instructions and robotic actions. Unlike traditional models in the field that demand comprehensive vision-language-action (VLA) datasets for training and lack interpretability, GeoManip operates in a training-free manner. It utilizes large foundational models to generate constraints and parse necessary geometry from the environment, then it uses these constraints to delineate precise manipulation trajectories. The system's ability to function without specific training for individual tasks allows for a level of versatility and generality that is otherwise challenging to achieve.

The system's architecture comprises three central components:

1. Geometry Parser: This identifies the object parts relevant to the geometric constraints using a select-process scheme. This parser selects and processes parts of objects that are analyzed and interpreted to extract essential geometric features.
2. Constraint Generator: Utilizing foundational geometric knowledge and task descriptions, this component outputs symbolic constraints required to execute each stage of a task.
3. Solver for Trajectory Optimization: This solver minimizes trajectory costs by ensuring alignment with the established geometric constraints, effectively guiding the robotic actions to meet task-specific goals.

Performance and Implications

Throughout extensive evaluations—including both virtual and real-world settings—GeoManip demonstrated its efficacy in performing state-of-the-art manipulations with robust out-of-distribution generalization. For instance, in virtual environments like MetaWorld and OmniGibson, and in real-world scenarios, GeoManip achieved superior results across tasks involving diverse object types and configurations.

Notably, the framework supports several advanced features critical for human-robot interaction and autonomous learning, such as:

• On-the-fly policy adaptation,
• Learning from human demonstrations and errors,
• Long-horizon action planning, and
• Efficient data collection for imitation learning.

These capabilities position GeoManip as a versatile tool for developing generalist robotic systems, where adaptability and precision are paramount. The training-free nature of the system offers significant cost and time savings, presenting practical advantages for implementing robotic solutions in dynamic and unpredictable environments.

Future Directions

GeoManip lays the groundwork for future research in robotics by suggesting potential directions for integrating geometric knowledge with machine learning models that require minimal task-specific training. Its approach of using geometric constraints presents an exciting possibility for enhancing autonomy and flexibility in robots, paving the way for further developments in AI-driven manipulation and interaction capabilities.

The paper provides a strong argument for the method's effectiveness, detailing its experimental success and suggesting that similar methodologies could be applied broadly in robotics. This aligns with ongoing efforts to democratize robot training and deployment across various industrial and commercial applications, addressing a critical need in the field. Future research could explore enhancing the system's decision-making capabilities, integrating more sophisticated machine learning techniques for constraint satisfaction, and expanding the range of manipulable objects and environments.