trajectory-prediction
4 free lessons tagged trajectory-prediction across Robotics. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Robustness and Deployment
What happens when a prediction model leaves the benchmark: adversarial fragility, distribution shift, the feedback loop where your own robot changes the behaviour it predicts, and how planners consume uncertainty safely.
Why the Standard Metrics Mislead
Displacement error is the field's default metric and it rewards the wrong behaviour: hedged average predictions, physically impossible trajectories, and best-of-many reporting that flatters diverse nonsense. What to measure instead.
Modelling Interaction: From Social Forces to Social Pooling
How the field learned to represent people influencing each other: the physics-inspired force model, the Social LSTM pooling layer that replaced hand-designed rules with learned ones, and the attention and graph architectures that followed.
Predicting Where People Will Walk
Why forecasting human motion is not a physics problem: the multimodality that makes a single correct answer impossible, the social conventions people navigate by, and the joint prediction problem where everyone is predicting everyone else.