Selected papers

• DEFT: Dexterous Fine-Tuning for Real-World Hand Policies

  Aditya Kannan*, Kenneth Shaw*, Shikhar Bahl, Pragna Mannam, Deepak Pathak

  CoRL 2023

  website | arXiv | abstract | bibtex

  Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to perform a host of skills with their hands, from making food to operating tools. In this paper, we investigate these challenges, especially in the case of soft, deformable objects as well as complex, relatively long-horizon tasks. Although, learning such behaviors from scratch can be data inefficient. To circumvent this, we propose a novel approach, DEFT (DExterous Fine-Tuning for Hand Policies), that leverages human-driven priors, which are executed directly in the real world. In order to improve upon these priors, DEFT involves an efficient online optimization procedure. With the integration of human-based learning and online fine-tuning, coupled with a soft robotic hand, DEFT demonstrates success across various tasks, establishing a robust, data-efficient pathway toward general dexterous manipulation.

  @article{kannan2023deft,
    title={DEFT: Dexterous Fine-Tuning for Real-World Hand Policies},
    author={Kannan, Aditya and Shaw, Kenneth and Bahl, Shikhar and Mannam, Pragna and Pathak, Deepak},
    journal={CoRL},
    year={2023}
  }

• Learning dexterity from human hand motion in internet videos

  Kenneth Shaw*, Shikhar Bahl*, Aravind Sivakumar, Aditya Kannan, Deepak Pathak

  IJRR 2024

  website | arXiv | abstract | bibtex

  To build general robotic agents that can operate in many environments, it is often useful for robots to collect experience in the real world. However, unguided experience collection is often not feasible due to safety, time, and hardware restrictions. We thus propose leveraging the next best thing as real world experience: videos of humans using their hands. To utilize these videos, we develop a method that retargets any 1st person or 3rd person video of human hands and arms into the robot hand and arm trajectories. While retargeting is a difficult problem, our key insight is to rely on only internet human hand video to train it. We use this method to present results in two areas: First, we build a system that enables any human to control a robot hand and arm, simply by demonstrating motions with their own hand. The robot observes the human operator via a single RGB camera and imitates their actions in real-time. This enables the robot to collect real-world experience safely using supervision. See these results at https://robotic-telekinesis.github.io. Second, we retarget in-the-wild human internet video into task-conditioned pseudo-robot trajectories to use as artificial robot experience. This learning algorithm leverages action priors from human hand actions, visual features from the images, and physical priors from dynamical systems to pretrain typical human behavior for a particular robot task. We show that by leveraging internet human hand experience, we need fewer robot demonstrations compared to many other methods. See these results at https://video-dex.github.io

  @article{shaw_internetvideos,
    title={Learning dexterity from human hand motion in internet videos},
    author={Shaw, Kenneth and Bahl, Shikhar and Sivakumar, Aravind and Kannan, Aditya and Pathak, Deepak},
    journal={International Journal of Robotics Research},
    year={2024}
  }