Robot Action Replanning With Multimodal LLM Feedback Correction
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Solution Overview
Problem
Current robotic manipulation systems struggle with generating accurate action sequences for daily tasks due to the lack of multimodal understanding and semantic representation, leading to imperfect action execution and distribution gaps between training and testing environments.
Innovation Solution
A multimodal large language model (LLM) is integrated with a query-transformer (Q-Former) to process diverse inputs like video, speech, and text, enabling error-correction-based robotic action replanning through human feedback, ensuring accurate sequence generation and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional machine learning models are used for robotic action generation, then the system can process basic tasks, but the action quality and granularity are insufficient for complex daily tasks
Solution Approach 1:
The patent introduces a Query Transformer as an intermediary component that bridges the gap between multimodal inputs (videos, speech, text) and the LLM-based action generation system. This intermediary processes and transforms diverse inputs into a unified representation that the LLM can effectively utilize, thereby improving action sequence quality while maintaining adaptability to complex tasks
Solution Approach 2:
The system employs a composite architecture combining multiple components: multimodal encoders for different input types, a Query Transformer for feature integration, and an LLM for action generation. This composite structure leverages the strengths of each component to achieve high-quality action sequences for complex daily tasks that single-model approaches cannot handle
2Loss of information
If LLMs are used for action sequence generation, then semantic understanding improves, but the generated actions may not correspond to intended actions due to lack of replanning capability
Solution Approach 1:
The patent implements a feedback mechanism where human feedback is incorporated into the action generation process. The system generates initial action sequences using the LLM, receives feedback on these sequences, and then refines them through iterative improvement. This feedback loop ensures that generated actions correspond to intended actions while maintaining high semantic understanding
Solution Approach 2:
The system performs preliminary action generation using the LLM to create initial action sequences with high semantic understanding. These preliminary sequences are then refined through feedback incorporation, allowing the system to maintain both semantic accuracy and execution reliability through a two-stage process
3Measurement precision
If human expert demonstrations are collected for training, then model performance improves, but the data collection process becomes extremely complex and tedious
Solution Approach 1:
The patent enables the system to learn from diverse existing data sources including videos, speech, and text without requiring complex human expert demonstrations. The multimodal architecture processes these readily available inputs directly, allowing the model to achieve high training accuracy while avoiding the tedious data collection process entirely
Solution Approach 2:
The system is designed to process multiple types of inputs (videos, speech, text) through a unified multimodal framework. This universal approach allows the model to learn from various data sources without requiring specialized collection procedures for each type, significantly reducing data collection complexity while maintaining high training accuracy
4Device complexity
If rigid input processing is used, then system simplicity is maintained, but adaptability to different task environments deteriorates
Solution Approach 1:
The patent implements dynamic adaptability through the Query Transformer, which can adjust its processing based on the specific task and input types. The system maintains a relatively simple core architecture while enabling dynamic adaptation to different environments and task requirements through configurable processing pathways and attention mechanisms
Data Source
AI summary
A robotic controller for controlling a robot according to a sequence of robotic actions. comprises an input interface to receive multimodal inputs specifying instructions for performing a task in audio, video, and a text modality. The controller transforms the multimodal instructions into encodings using a large language model (LLM) encoder and decodes the encodings into a first sequence of robotic instructions and a robot action description of the actions using an LLM decoder. Human feedback input is received corresponding to at least one action in the first sequence of actions and the controller encodes the feedback input with the robot action description. The controller feeds the encoded data along with multimodal features generated from the encodings into the LLM decoder to generate a corrected sequence of actions. The controller is configured to control a robot according to the corrected sequence of actions.