Results for "demonstration-based"

Storing results to reduce compute.

AI systems that perceive and act in the physical world through sensors and actuators.

Devices measuring physical quantities (vision, lidar, force, IMU, etc.).

Internal sensing of joint positions, velocities, and forces.

External sensing of surroundings (vision, audio, lidar).

Control using real-time sensor feedback.

Control without feedback after execution begins.

Using output to adjust future inputs.

Classical controller balancing responsiveness and stability.

Computing end-effector position from joint angles.

Performance drop when moving from simulation to reality.

RL without explicit dynamics model.

Learning physical parameters from data.

Optimizing continuous action sequences.

Learning action mapping directly from demonstrations.

Computing collision-free trajectories.

Finding routes from start to goal.

Learned model of environment dynamics.

Optimal pathfinding algorithm.

Imagined future trajectories.

Understanding objects exist when unseen.

Human-like understanding of physical behavior.

Inferring human goals from behavior.

Ensuring robots do not harm humans.

Closed loop linking sensing and acting.

AI systems assisting clinicians with diagnosis or treatment decisions.

AI that ranks patients by urgency.

Grouping patients by predicted outcomes.

US approval process for medical AI devices.

AI-assisted review of legal documents.