Nvidia corporation (20240160888). REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS simplified abstract
Contents
- 1 REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS
Organization Name
Inventor(s)
Davis Winston Rempe of Redwood CA (US)
Karsten Julian Kreis of Vancouver (CA)
Or Litany of Sunnyvale CA (US)
REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160888 titled 'REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS
Simplified Explanation
The patent application discusses neural networks for realistic and controllable agent simulation using guided trajectories. The neural networks are trained using data related to subjects or agents, their trajectories, neighboring subjects, and environmental context data. The trajectories are determined using the neural networks with guidance for controllability, such as waypoint navigation, obstacle avoidance, and group movement.
- Neural networks for agent simulation with guided trajectories
- Training data includes trajectories, state data, and context data
- Trajectories determined using neural networks with guidance for controllability
Potential Applications
This technology could be applied in various fields such as:
- Robotics
- Autonomous vehicles
- Video game development
- Virtual reality simulations
Problems Solved
This technology addresses the following issues:
- Realistic agent simulation
- Controllable agent behavior
- Efficient trajectory planning
Benefits
The benefits of this technology include:
- Enhanced realism in simulations
- Improved controllability of agents
- Efficient and optimized trajectory planning
Potential Commercial Applications
This technology has potential commercial applications in:
- Entertainment industry
- Military simulations
- Traffic management systems
- Industrial automation
Possible Prior Art
One possible prior art in this field is the use of traditional algorithms for trajectory planning in agent simulations. However, neural networks offer a more advanced and efficient approach to this problem.
What are the limitations of using traditional algorithms for trajectory planning in agent simulations?
Traditional algorithms may struggle with complex environments and dynamic scenarios, leading to less realistic agent behavior.
How does the use of neural networks improve controllability in agent simulations compared to traditional methods?
Neural networks can adapt and learn from data, allowing for more flexible and responsive agent behavior in various situations.
Original Abstract Submitted
in various examples, systems and methods are disclosed relating to neural networks for realistic and controllable agent simulation using guided trajectories. the neural networks can be configured using training data including trajectories and other state data associated with subjects or agents and remote or neighboring subjects or agents, as well as context data representative of an environment in which the subjects are present. the trajectories can be determining using the neural networks and using various forms of guidance for controllability, such as for waypoint navigation, obstacle avoidance, and group movement.
