arXiv:2606.12365v1 Announce Type: cross Abstract: We propose Ambient Diffusion Policy, a simple and principled method for imitation learning from suboptimal data in robotics. High-quality, task-specific robot data is expensive and time-consuming to collect, while suboptimal datasets with lower-quality or out-of-distribution demonstrations are abundant. Existing methods that co-train on both data sources in robotics often fail to separate the meaningful and the harmful features in the suboptimal samples. In contrast, our method extracts only the useful features by introducing a new axis to co-t
This development addresses a critical bottleneck in robotics training, leveraging abundant suboptimal data to accelerate learning without requiring expensive, high-quality datasets.
Robotics development has been hampered by the cost and difficulty of data collection; this method could significantly lower barriers to entry and accelerate the deployment of autonomous systems.
The ability to effectively use suboptimal demonstration data fundamentally alters the data acquisition strategy for robotic imitation learning, making it more scalable and less resource-intensive.
- · Robotics companies
- · AI researchers
- · Automation sector
- · Companies relying on expensive, bespoke data collection for robotics
- · Traditional imitation learning methods
More robust and generalizable robotic policies can be developed faster and at lower cost.
This could lead to a proliferation of more capable and affordable robotic applications across various industries.
Accelerated development in robotics could hasten the integration of physical AI into daily life and industrial processes, impacting labor markets and societal structures.
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Read at arXiv cs.AI