TL;DR
HOST acquires skills at inference time, each from a single human video, averaging 29 seconds, while retaining its previously mastered skills.
HOST acquires skills at inference time, each from a single human video, averaging 29 seconds, while retaining its previously mastered skills.
This work introduces Video2Skill, a benchmark that covers robot tabletop manipulation and human kitchen activity and tests three core capabilities: locating manipulation events in time, grouping events of the same transformation, and deciding when to reuse an existing skill or create a new one.
This work introduces RoboReel: a unified benchmark for evaluating models that learn policies from human videos, and develops four test suites to evaluate the models' performance on multiple axes, including the robustness to visual distractors and the ability to complete long-horizon tasks.
Learning humanoid skills from videos typically requires a successful human demonstration, which often demands custom data collection. Although failures have traditionally been treated only as negative examples in robot learning, they can still reveal a usable trajectory prefix before the task fails, as well as the inte...
P2R tracks task-relevant object points in task demonstrations and policy rollouts, and matches them to compare the desired and achieved object trajectories to generate reward scores, which effectively computes dense rewards from a single video.
CHOREO, a framework for training-free composition of heterogeneous humanoid skills, demonstrates that executable trajectories provide a scalable interface for accumulating and composing pretrained humanoid capabilities.
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