Human Demonstrations are Generalizable Knowledge for Robots
(2312.02419)Abstract
Learning from human demonstrations is an emerging trend for designing intelligent robotic systems. However, previous methods typically regard videos as instructions, simply dividing them into action sequences for robotic repetition, which poses obstacles to generalization to diverse tasks or object instances. In this paper, we propose a different perspective, considering human demonstration videos not as mere instructions, but as a source of knowledge for robots. Motivated by this perspective and the remarkable comprehension and generalization capabilities exhibited by LLMs, we propose DigKnow, a method that DIstills Generalizable KNOWledge with a hierarchical structure. Specifically, DigKnow begins by converting human demonstration video frames into observation knowledge. This knowledge is then subjected to analysis to extract human action knowledge and further distilled into pattern knowledge compassing task and object instances, resulting in the acquisition of generalizable knowledge with a hierarchical structure. In settings with different tasks or object instances, DigKnow retrieves relevant knowledge for the current task and object instances. Subsequently, the LLM-based planner conducts planning based on the retrieved knowledge, and the policy executes actions in line with the plan to achieve the designated task. Utilizing the retrieved knowledge, we validate and rectify planning and execution outcomes, resulting in a substantial enhancement of the success rate. Experimental results across a range of tasks and scenes demonstrate the effectiveness of this approach in facilitating real-world robots to accomplish tasks with the knowledge derived from human demonstrations.
Overview
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The paper introduces a new method, DigKnow, for teaching robots using human demonstration videos as a source of generalizable knowledge.
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DigKnow breaks down videos to create a hierarchical knowledge system, enhancing robots' adaptability and performance in various tasks.
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The approach uses LLMs to help robots plan actions and correct mistakes based on the learned knowledge from human demonstrations.
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Real-world experiments show that DigKnow enables robots to generalize skills from human demonstrations, although further testing is required.
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DigKnow marks a significant advancement in robot learning methodologies by transforming human demonstrations from sequences to actionable knowledge.
Introduction
The integration of human demonstration videos into robotic system learning represents an innovative approach within the field of robotics. Instead of simply replicating actions, human demonstrations are now viewed as a repository of knowledge from which robots can draw to perform various tasks. This process entails converting human actions observed in videos into a structured form of knowledge that robots can understand and adapt to different situations.
Knowledge Distillation Approach
A new methodology called DigKnow has been developed to process and sublimate human demonstration videos into hierarchical knowledge, which can be accessed and used by robots. The process begins with the analysis of video frames to extract 'observation knowledge', which includes understanding the spatial relationships within the scene. Subsequent stages involve generating 'action knowledge' through keyframe analysis and, ultimately, distilling this information into 'pattern knowledge', which divides into task-specific and object-specific insights. Importantly, this hierarchical knowledge enables robots to better generalize and adapt to new environments or tasks.
Knowledge Retrieval and Correction
When facing new tasks or objects, the robots tap into the stored knowledge to formulate a plan that aligns with the current requirements. This planning process is facilitated by employing LLMs, which assist in interpreting and integrating the relevant knowledge to create actionable sequences. Moreover, DigKnow features a correction component that leverages the gathered knowledge to validate and correct action plans and executions, thereby optimizing the robot's performance and adaptability.
Experiments and Results
The efficacy of DigKnow has been assessed through real-world experiments using diverse tasks and environmental setups. These assessments demonstrate the system's proficiency in generalizing skills derived from human demonstrations across various contexts. It should be noted, however, that the current scope of testing is rather limited. Future expansions of experimental setups are planned to comprehensively validate DigKnow's performance.
Conclusion
DigKnow represents a significant advance in robot learning methodologies by leveraging human demonstrations as a rich source of knowledge, rather than mere sequential instructions. Its hierarchical knowledge structure enables robots to retrieve relevant information for novel tasks and objects, while its correction mechanisms help in achieving a high success rate even in unfamiliar scenarios. If further testing confirms these early results, DigKnow holds the potential to greatly enhance a robot's ability to perform complex tasks informed by human experiences.
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