EgoExplore is a real-world dataset of egocentric video sequences capturing open-world exploration with a strong emphasis on embodied physics, object interaction, and multi-agent dynamics. Collected using head-mounted GoPro cameras in natural, unstructured environments, the dataset offers rich, continuous trajectories ideal for training and evaluating world models, embodied agents, and multimodal perception systems.

Unlike scripted or task-bounded datasets, EgoExplore features freeform exploration through diverse physical scenes, where human behavior, object manipulation, and environmental physics unfold naturally and unsupervised.

The dataset is especially rich across three dimensions:

• Object Interaction – manipulating real-world objects like gas pumps, car trunks, gym equipment, trash bins, and rideshare bicycles

• Human Action – locomotion (walking, running, biking), physical exertion (pull-ups), fine motor actions (opening, lifting, discarding), and transitions across modalities (walking → driving → biking)

• Behavior of Other Agents – close-range, unpredictable interactions with other autonomous agents like a dog (peeing, jumping, sniffing) and human drivers (Uber/Lyft)

Two samples are currently included in the sample dataset:

• Suburban & Terrain Exploration (Figure 1, 51 minutes): A person walks his dog through a suburban neighborhood and hilly terrain, capturing complex agent-environment interactions—dog behavior, human response, object use (car trunk, trash bin), and urban navigation (driving to a gas station, handling pump failure).

• Urban Multimodal Journey (Figure 2, 52 minutes): A person enters an Uber, exercises at an outdoor gym (e.g. pull-ups on public bars), explores the city on foot, and ends with a Lyft bike rental. This sequence captures transitions across transportation modes, public infrastructure use, and diverse human-object contact.

Each sequence is captured in high-resolution and provides continuous physical context, with real-world constraints, cause-and-effect, and emergent behavior. The dataset supports research in:

• Learning physics-aware world models from egocentric data

• Modeling temporal and spatial continuity of embodied action

• Understanding object affordances and agent-driven environmental changes

• Capturing multi-agent dynamics in open, unpredictable settings

The untrimmed, immersive nature of EgoExplore makes it well-suited for advancing generalist agents and sensorimotor learning beyond closed benchmarks.