Adaptation of Generalist Robot Policies with Minimal Data
Bridging the gap between static pre-trained models and autonomous robot learning with single-shot adaptation.
A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet fully autonomous learning remains difficult with current policies: sparse rewards and weak zero-shot exploration make it unlikely ...
ArXiv CS.RO
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MiDAS represents a critical leap in robotics by demonstrating that machines can refine complex behaviors from a single human example through autonomous online interaction. This shift reduces the dependency on massive, curated datasets, paving the way for robots that evolve and adapt to new environments in real-time.
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