Robot Affordance Plans for Precise Manipulation Control
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Solution Overview
Problem
Existing robot policies struggle to generalize to various tasks and environments due to the lack of spatially precise and dimensionally concise guidance, with current methods being either underspecified or over-specified, leading to inefficiencies and resource-intensive data collection.
Innovation Solution
Implementing an affordance-conditioned policy model that generates actions based on language instructions, current images, and affordance plans, which are sequences of intermediate robot end effector poses, providing precise spatial information for manipulation tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If goal images are used to provide spatial information, then spatial precision is improved, but input dimensionality and complexity increase
Solution Approach 1:
The patent extracts only the essential spatial information needed for task execution from the full goal image, using keypoint annotations that identify critical locations and orientations. This extraction approach maintains spatial precision while dramatically reducing input dimensionality by focusing only on relevant geometric features rather than the complete image data.
Solution Approach 2:
The patent transforms the high-dimensional image input into a lower-dimensional keypoint representation by projecting 3D object poses and orientations onto 2D image coordinates. This dimensional transformation preserves the essential spatial relationships needed for manipulation while reducing the data complexity from full image resolution to discrete keypoint coordinates.
2Reliability
If detailed trajectory sketches are provided to guide manipulation, then task guidance is improved, but user burden and specification complexity increase
Solution Approach 1:
The system enables automatic generation of affordance plans from natural language instructions and goal images without requiring manual trajectory specification. The affordance prediction model automatically infers the sequence of intermediate poses and keypoints needed to accomplish the task, allowing the system to serve itself rather than requiring detailed user specification while maintaining reliable task guidance.
3Reliability
If extensive robot demonstration data is collected to improve performance, then model accuracy is improved, but resource consumption and cost increase
Solution Approach 1:
The patent pre-trains the affordance prediction model on large-scale web datasets containing images and natural language descriptions of objects and their affordances. This preliminary training on abundant web data provides the model with general knowledge about object properties and manipulation possibilities, reducing the need for extensive robot-specific demonstration data while maintaining model accuracy.
Solution Approach 2:
The system leverages web-scale image and text data as proxies for robot demonstration data. By training on copied data from the internet rather than collecting original robot trajectories, the model acquires general manipulation knowledge at lower resource cost while the affordance plan generation adapts this knowledge to specific robot tasks.
Data Source
AI summary
Implementations for robot control are provided. A method involves, based on vision data depicting an environment of a robot and a natural language instruction for the robot, determining an affordance plan for performing a task. The affordance plan comprises a sequence of intermediate representations of the robot in visual space, such as end effector poses. An action input prompt is assembled with data indicative of the vision data, the natural language instruction, and the affordance plan. The action input prompt is processed using one or more generative models to generate action output indicative of one or more actions to be performed by the robot. Subsequently, a robot control signal is generated based on the one or more actions. This provides a spatially precise and dimensionally concise form of guidance for robot manipulation tasks, which can improve performance and generalization.