{"author_name":"Paper Radio","author_url":"https://montaan.com/","height":720,"html":"<iframe src=\"https://montaan.com/embed/video/9ac8eaaf-c1c6-4d5f-b32f-c3ebd887acdc\" width=\"1280\" height=\"720\" title=\"PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning — Paper Radio\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture\" allowfullscreen loading=\"lazy\" style=\"border:0;display:block;max-width:100%;\"></iframe>","provider_name":"Paper Radio","provider_url":"https://montaan.com/","thumbnail_url":"https://montaan.com/media/episodes/9ac8eaaf-c1c6-4d5f-b32f-c3ebd887acdc/episode.poster.webp","title":"PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning","type":"video","version":"1.0","width":1280}