Language-Vision Reward Shaping for Robotic Policy Learning
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Solution Overview
Problem
Existing robotic learning control methods face challenges in creating reward functions that provide meaningful feedback without requiring historical training data or extensive handcrafting, especially in dynamic manufacturing environments where tasks frequently change, and there is a lack of optimal methods for phrasing language instructions to guide policy learning.
Innovation Solution
A method involving image slicing, embedding, and distribution generation based on relevance to task descriptions, using inverse reinforcement learning to create a reward signal that updates robotic policies without historical data, optimizing task descriptions through semantic embedding and entropy-based reward calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If dense rewards are used to provide more information about intermediate steps, then the feedback signal quality is improved, but the difficulty of constructing the reward function increases and requires more handcrafting
Solution Approach 1:
The system uses the language instruction itself to automatically generate the reward shaping function through embedding models, eliminating the need for external handcrafting. The language instruction serves dual purposes: guiding the task and defining the reward structure automatically.
Solution Approach 2:
The manual process of crafting reward functions is replaced by an automated embedding model that processes language instructions and directly generates potential functions, substituting human engineering effort with computational processing.
2Reliability
If historical training data is collected to learn potential functions for reward shaping, then the reward function quality is improved, but the time and resources required to build the shaping function increase
Solution Approach 1:
The embedding model is pre-trained on general language-data pairs, so when a new language instruction is provided, the system can immediately generate an appropriate reward shaping function without requiring task-specific historical data collection or training time.
Solution Approach 2:
A single embedding model serves multiple functions: it processes various language instructions across different tasks and consistently generates appropriate reward shaping functions, making the system universally applicable without task-specific customization.
3Productivity
If language instructions are optimized through prompt engineering to guide policy learning, then the learning effectiveness is improved, but the complexity of phrasing and optimizing instructions increases
Solution Approach 1:
The system uses the language instruction as feedback to the embedding model, which then generates a reward shaping function that reinforces the intended meaning. This creates a closed loop where the instruction quality directly influences the learned policy through the automatically generated reward signal.
4Ease of manufacture
If sparse rewards are used to simplify reward function construction, then the ease of implementation is improved, but the information content of the feedback signal decreases
Solution Approach 1:
The embedding model acts as an intermediary that translates simple language instructions into rich reward shaping functions. This mediator bridges the gap between simple implementation (sparse reward structure) and rich information content (dense reward signals) by automatically generating appropriate intermediate reward signals.
Data Source
AI summary
Example implementations described herein involve systems and methods for providing a reward to a machine learning algorithm, which can include receiving an image, and a task description defined in text; slicing the image into a plurality of sub-images; executing an embedding model to embed the text of the task description and the sub-images to generate a distribution for the sub-images based on relevance to the task description; and generating the reward from the distribution for the sub-images.