{"url":"/dataset/robopianist","name":"RoboPianist","full_name":null,"description_markdown":"**RoboPianist** is a benchmarking suite for high-dimensional control, targeted at testing high spatial and temporal precision, coordination, and planning, all with an underactuated system frequently making-and-breaking contacts. The proposed challenge is mastering the piano through bi-manual dexterity, using a pair of simulated anthropomorphic robot hands. The initial version covers a broad set of 150 variable-difficulty songs.\r\n\r\nSource: [RoboPianist: A Benchmark for High-Dimensional Robot Control](https://arxiv.org/pdf/2304.04150v1.pdf)\r\n\r\nImage Source: [RoboPianist: A Benchmark for High-Dimensional Robot Control](https://arxiv.org/pdf/2304.04150v1.pdf)","description_withheld":null,"homepage":"https://github.com/google-research/robopianist/","introduced_date":"2023-04-09","introduced_date_note":null,"introduced_by":{"paper":"/paper/robopianist-a-benchmark-for-high-dimensional","title":"RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning","first_author":"Kevin Zakka","url":null},"license":{"name":"Apache-2.0 license","url":"https://github.com/google-research/robopianist/blob/main/LICENSE"},"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[{"name":"Multi-Task Learning","url":"/task/multi-task-learning","datasets_with_task":"/datasets/task/multi-task-learning"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["RoboPianist"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}