Interview with Dai Ruoli: ModalityNet compiles human data into learnable robot data

Shima Notes conducted an in-person interview with Noitom founder Dai Ruoli, interpreting the ModalityNet dataset it has launched. The dataset contains three types of data that can be reused across embodiments: MOV large-space interaction data (about 8,000 hours, compared with about 70-plus hours for AMASS), OM dexterous manipulation data (sub-millimeter hand precision, including tactile and spatial audio), and ITW in-the-wild subjective vision data; the public portion is about 1/20 of the amount actually held.