Skill: ego-motion-and-trajectory-estimation

[Case 2]

• Initial Environment: Two observations from the same tabletop scene are captured before and after a controlled camera motion. The scene content barely changes except for parallax across a box, a mug, and a desk edge.
• Real Question: How many degrees did the camera rotate in yaw?
• Real Answer: 47 degrees.
• Why this demonstrates the capability: The objects themselves are static, so the task is specifically about ego-motion rather than object motion. The agent must estimate orientation change from viewpoint variation. This directly targets ego-centric orientation reasoning.

[Case 3]

• Initial Environment: An outdoor driving clip shows the camera moving past parked vehicles, then slowing near an intersection. Time stamps are available and the visible scene contains lane markers and moving cars.
• Real Question: How far has the car traveled from 1 second to 18 seconds?
• Real Trajectory: The agent tracks the camera path over time, aggregates displacement across segments, and ignores irrelevant object motion from nearby traffic.
• Real Answer: 63 meters.
• Why this demonstrates the capability: The problem requires separating self-motion from the motion of other objects. It also requires integrating spatial change over time rather than making a single-frame guess. This is a core dynamic capability for embodied agents operating in the world.

Pipeline Execution Instructions

To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:

1. Phase 1: Environment Exploration Read the exploration guidelines to discover raw knowledge seeds: references/EXPLORATION.md

2. Phase 2: Trajectory Selection Once Phase 1 is complete, read the selection criteria to evaluate the trajectory: references/SELECTION.md

3. Phase 3: Data Synthesis Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data: references/SYNTHESIS.md