Synthesizing Human Object Interactions with High-level Instructions
In this comprehensive system, we showcase the synthesis of physically plausible human-object interactions based on high-level instructions, emphasizing the importance of generating synchronized object, human, and finger motions. By instructing a human to arrange boxes, our system successfully orchestrates detailed movements, blending a high-level planner with a low-level motion generator to ensure the physical plausibility of actions.
Elements of the System
Our system intricately weaves together different components to achieve seamless interactions. Firstly, a high-level planner based on LLM processes human-level instructions, leading to the creation of a scene map and an execution plan. Subsequently, a low-level motion generator harmonizes object, human body, and finger motions in a synchronized manner. To guarantee realistic outcomes, a physics tracker, employing reinforcement learning techniques, validates and refines the generated motions for physical accuracy.
Performance Evaluation
Comparisons with baseline models such as CNET Plus highlight the superiority of our methodology in generating precise hand and finger motions. In contrast, the baselines struggle to replicate natural interactions, showcasing the effectiveness of our system in producing authentic finger movements. Additionally, comparisons with ablations like CNET and C+RNET underscore the realism our system offers, particularly in simulating intricate finger motions absent in the ablations.
Long Sequence Generation
The complexity of human-object interactions is further exemplified through long sequence tasks. From cleaning an area to preparing Christmas presents and organizing a workspace, our system showcases a deep understanding of contextual tasks. By accurately identifying objects in various scenarios, our system proficiently executes tasks, highlighting a remarkable grasp of common sense and physical concepts.
Conclusion
Through a blend of high-level planning, motion generation, and physics tracking, our system demonstrates a remarkable ability to synthesize human-object interactions authentically. From stacking boxes to arranging workspaces and organizing shoes, the system showcases a nuanced understanding of tasks, enriching the realm of human-AI interactions with practical applications that mirror real-world scenarios.