Multimodal Robot Planning With LLMs for Reliable Action Sequences
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Solution Overview
Problem
Current robotic systems struggle to generate high-quality action sequences for everyday tasks due to the inability to effectively process and integrate diverse modalities of human instructions, leading to suboptimal performance in real-world environments.
Innovation Solution
A multimodal large language model (LLM) is employed to process video, audio, and text inputs, using a multimodal encoder and query-transformer to generate robotic instructions, integrated with an action sequence decoder to produce actionable sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning models are used for robotic action generation, then the system can process single-modality inputs, but it fails to integrate diverse modalities (video, audio, text) effectively
Solution Approach 1:
The patent combines multiple modality-specific encoders (video encoder, audio encoder, text encoder) into a unified multimodal encoder that processes all inputs simultaneously and produces a single integrated embedding representation. This merging allows the system to effectively integrate diverse modalities while maintaining high action sequence quality through the unified representation.
Solution Approach 2:
The patent employs a universal large language model that can process embeddings from multiple modalities through a single interface. The LLM serves multiple functions: understanding visual demonstrations, interpreting audio instructions, processing text inputs, and generating action sequences, thereby achieving versatility without sacrificing reliability.
2Productivity
If human expert demonstrations are collected for training, then the robot can learn complex manipulation tasks, but the data collection process becomes extremely complex and time-consuming
Solution Approach 1:
The patent uses video demonstrations as copies of human actions rather than requiring direct robot execution of complex demonstration protocols. The video encoder extracts action information from visual copies of human performance, significantly simplifying data collection while maintaining the ability to learn complex manipulation tasks.
Solution Approach 2:
The patent introduces a multimodal encoder and large language model as intermediaries between raw demonstration data and robot control. These intermediaries automatically process and translate diverse inputs (videos, audio, text) into structured action representations, eliminating the need for complex manual data annotation and simplifying the training process.
3Reliability
If rigid input structures are used for action generation, then the system can maintain consistent processing, but it becomes too restrictive for diverse real-world scenarios
Solution Approach 1:
The patent implements a dynamic input processing system where the multimodal encoder can accept variable combinations of modalities (video only, audio only, text only, or any combination) and adaptively process them. The system dynamically adjusts which encoders are activated based on available inputs, maintaining consistent processing through the unified LLM interface while being highly flexible for diverse scenarios.
4Device complexity
If the robot controller directly processes raw inputs, then the system architecture remains simple, but it cannot generate high-quality semantic action sequences
Solution Approach 1:
The patent introduces a multimodal encoder and large language model as intermediary processing layers between raw inputs and the robot controller. These intermediaries perform sophisticated semantic understanding and action generation, producing high-precision action sequences while keeping the robot controller itself relatively simple in its execution function.
Data Source
AI summary
A robotic controller for controlling a robot according to a sequence of robotic actions. comprises an input interface configured to receive a plurality of multimodal inputs each specifying instructions for performing a task in a different modality including audio, video, and a text modality. The controller also comprises a multimodal large language model, an action sequence decoder, and a controller. The multimodal LLM includes a multimodal LLM encoder and an LLM decoder. The multimodal LLM encoder is trained with machine learning to transform the multimodal instructions into encodings and the LLM decoder is configured to decode the encodings into a sequence of robotic instructions. The action sequence decoder is trained with machine learning to transform the sequence of robotic instructions into a sequence of actions using a library of robotic skills. The controller is configured to control a robot according to the sequence of actions.