The Role of Human-in-the-Loop Preferences in Reward Function Learning for Humanoid Tasks | HackerNoon
Briefly

from Hackernoon3 months ago
In human-in-the-loop preference experiments for tasks in IsaacGym, volunteers provided feedback by comparing videos of performance based on different reward functions.
Hackernoonhttps://hackernoon.com/the-role-of-human-in-the-loop-preferences-in-reward-function-learning-for-humanoid-tasks
Volunteers assess the performance of the Quadcopter by its speed and stabilization, while the Humanoid and Ant tasks focus on speed and movement posture.
Hackernoonhttps://hackernoon.com/the-role-of-human-in-the-loop-preferences-in-reward-function-learning-for-humanoid-tasks
In the ShadowHand and AllegroHand tasks, the goal is to orient an object correctly, with humans estimating performance based on the proximity to the target orientation.
Hackernoonhttps://hackernoon.com/the-role-of-human-in-the-loop-preferences-in-reward-function-learning-for-humanoid-tasks
Despite variability in judgments, volunteers effectively filter out poor results, aligning their selections with proxy human preferences to enhance task performance.
Hackernoonhttps://hackernoon.com/the-role-of-human-in-the-loop-preferences-in-reward-function-learning-for-humanoid-tasks
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#human-in-the-loop #isaacgym #robotic-tasks #performance-evaluation #machine-learning
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