RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2304.04150v3
• Date: Mon, 4 Dec 2023 01:14:58 GMT
• Title: RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
• Authors: Kevin Zakka, Philipp Wu, Laura Smith, Nimrod Gileadi, Taylor Howell, Xue Bin Peng, Sumeet Singh, Yuval Tassa, Pete Florence, Andy Zeng, Pieter Abbeel
• Abstract summary: We introduce RoboPianist, a system that enables simulated anthropomorphic hands to learn an extensive repertoire of 150 piano pieces. We additionally introduce an open-sourced environment, benchmark of tasks, interpretable evaluation metrics, and open challenges for future study.
• Score: 61.10744686260994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics. Reinforcement learning is a promising approach that has achieved impressive progress in the last few years; however, the class of problems it has typically addressed corresponds to a rather narrow definition of dexterity as compared to human capabilities. To address this gap, we investigate piano-playing, a skill that challenges even the human limits of dexterity, as a means to test high-dimensional control, and which requires high spatial and temporal precision, and complex finger coordination and planning. We introduce RoboPianist, a system that enables simulated anthropomorphic hands to learn an extensive repertoire of 150 piano pieces where traditional model-based optimization struggles. We additionally introduce an open-sourced environment, benchmark of tasks, interpretable evaluation metrics, and open challenges for future study. Our website featuring videos, code, and datasets is available at https://kzakka.com/robopianist/

Related papers

• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation [50.616995671367704]
  We present a high-dimensional, simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot equipped with dexterous hands. Our findings reveal that state-of-the-art reinforcement learning algorithms struggle with most tasks, whereas a hierarchical learning approach achieves superior performance when supported by robust low-level policies.
  arXiv  Detail & Related papers  (2024-03-15T17:45:44Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• Learning Perceptual Concepts by Bootstrapping from Human Queries [41.07749131023931]
  We propose a new approach whereby the robot learns a low-dimensional variant of the concept and uses it to generate a larger data set for learning the concept in the high-dimensional space. This lets it take advantage of semantically meaningful privileged information only accessible at training time, like object poses and bounding boxes, that allows for richer human interaction to speed up learning.
  arXiv  Detail & Related papers  (2021-11-09T16:43:46Z)
• The Ingredients of Real-World Robotic Reinforcement Learning [71.92831985295163]
  We discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world. We propose a particular instantiation of such a system, using dexterous manipulation as our case study. We demonstrate that our complete system can learn without any human intervention, acquiring a variety of vision-based skills with a real-world three-fingered hand.
  arXiv  Detail & Related papers  (2020-04-27T03:36:10Z)
• Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction [0.2549905572365809]
  We have proposed an Inception module based Convolutional Neural Network Architecture. The model is implemented in a humanoid robot, NAO in real time and robustness of the model is evaluated.
  arXiv  Detail & Related papers  (2020-01-12T05:25:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.