First-Person Activity Forecasting with Online Inverse Reinforcement Learning
This addresses the problem of long-term activity forecasting for first-person camera wearers, representing an incremental advance over prior trajectory forecasting methods by incorporating semantic and goal reasoning.
The paper tackles the problem of incrementally modeling and forecasting long-term goals from first-person visual observations, such as what a user will do, where they will go, and what goal they seek, using an Online Inverse Reinforcement Learning approach called DARKO, which shows better goal forecasting than competing methods in noisy and ideal settings.
We address the problem of incrementally modeling and forecasting long-term goals of a first-person camera wearer: what the user will do, where they will go, and what goal they seek. In contrast to prior work in trajectory forecasting, our algorithm, DARKO, goes further to reason about semantic states (will I pick up an object?), and future goal states that are far in terms of both space and time. DARKO learns and forecasts from first-person visual observations of the user's daily behaviors via an Online Inverse Reinforcement Learning (IRL) approach. Classical IRL discovers only the rewards in a batch setting, whereas DARKO discovers the states, transitions, rewards, and goals of a user from streaming data. Among other results, we show DARKO forecasts goals better than competing methods in both noisy and ideal settings, and our approach is theoretically and empirically no-regret.