The authors trained across heterogeneous tasks including peg insertion, gear meshing, connector insertion, and nut threading.
Sparse rewards teach one robot policy all 20 NIST assemblies
In simulation, AlphaNIST learned contact-rich assembly and multi-part sequencing without demonstrations, shaped progress rewards, or prescribed task order.
Big Tech
Octi Zhang · Iretiayo Akinola · Rosario Scalise · Mateo Guaman Castro · Patrick Yin · Tobias Widmer · +9 more
NVIDIA · University of Washington
Research Digest··2 min read
Zhang et al.
Why this paper
From NVIDIA and University of Washington
In one line
A single reinforcement learning policy can learn all 20 NIST Assembly Task Board 1 tasks from sparse binary rewards, reaching 98.2% training success without demonstrations or shaping.
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