Robot Task Planning From Complex Instructions and Visual Cues
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Solution Overview
Problem
Robots struggle to interpret complex human language instructions for tasks that require multiple discrete actions, as human language is ambiguous and context-dependent, leading to difficulties in determining the appropriate sequence of actions needed to achieve a long-term goal.
Innovation Solution
A system that combines symbolic task planning with natural language understanding and visual inputs to generate a sequence of discrete tasks for robotic manipulation, using neural networks to analyze audio and image data to infer predicates and plan optimal task sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robots use conventional approaches to interpret human language instructions, then the system complexity remains low, but the robot cannot accurately interpret complex or ambiguous instructions
Solution Approach 1:
The system segments the complex task interpretation problem into distinct components: visual scene understanding, audio instruction processing, predicate inference, and task sequence planning. Each component is handled by specialized modules that process specific aspects of the input independently before integrating results, thereby improving accuracy without proportionally increasing overall system complexity.
Solution Approach 2:
The system introduces intermediate representations including visual scene graphs from image data, audio transcriptions from speech, and inferred predicates that bridge the gap between raw sensory inputs and task planning. These intermediaries structure the information in a form that is easier to process and reason about, enhancing interpretation accuracy while managing computational complexity.
2Adaptability or versatility
If robots require explicit step-by-step instructions for each action, then the instructions are unambiguous, but this lacks adaptability to natural human communication styles
Solution Approach 1:
The system performs preliminary analysis of the visual scene and audio instructions to infer missing task details before generating the final task sequence. By pre-processing the inputs to extract predicates, objects, and relationships, the system recovers incomplete information from natural language instructions without requiring explicit step-by-step specification from the user.
Solution Approach 2:
The system uses visual feedback from the scene understanding module to disambiguate referents in audio instructions (e.g., determining which drawer or knife is being referenced). The visual scene provides contextual feedback that resolves ambiguities in natural language, enabling the system to adapt to human communication styles while recovering complete task specifications.
3Ease of operation
If robots infer which specific objects and actions are needed from complex tasks, then the robot can handle natural language instructions, but the computational difficulty increases
Solution Approach 1:
The system segments the inference process into distinct stages: visual object detection and scene graph construction, audio transcription and entity recognition, predicate inference from combined inputs, and task sequence generation. This segmentation reduces the computational difficulty of each individual inference step compared to attempting to solve the entire problem in one complex process.
Solution Approach 2:
The system introduces visual scene graphs and audio transcriptions as intermediary representations that structure the raw sensory data before task inference. These intermediaries make the inference process more manageable by organizing information in standardized formats that facilitate automated reasoning about which objects and actions are needed.
Data Source
AI summary
Approaches provide for performance of a complex (e.g., compound) task that may involve multiple discrete tasks not obvious from an instruction to perform the complex task. A set of conditions for an environment can be determined using captured image data, and the instruction analyzed to determine a set of final conditions to exist in the environment after performance of the instruction. These initial and end conditions are used to determine a sequence of discrete tasks to be performed to cause a robot or automated device to perform the instruction. This can involve use of a symbolic or visual planner in at least some embodiments, as well as a search of possible sequences of actions available for the robot or automated device. A robot can be caused to perform the sequence of discrete tasks, and feedback provided such that the sequence of tasks can be modified as appropriate.