ORCA: A Platform for Open-Source Dexterity Research

• URL: http://arxiv.org/abs/2606.14561v1
• Date: Fri, 12 Jun 2026 15:38:34 GMT
• Title: ORCA: A Platform for Open-Source Dexterity Research
• Abstract summary: orcalearning stack is an open-source research stack for dexterity as a first-class robot learning domain.<n>orcastack unifies low-level control, simulation, teleoperation from a range of consumer platforms.<n>We demonstrate a complete end-to-end workflow, collecting expert demonstrations of an in-hand reorientation task by teleoperation with a consumer-grade VR headset.
• Score: 0.04666493857924358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robotics manipulation research increasingly focuses on two-finger parallel grippers for their effectiveness, affordability, and ease of teleoperation. Grippers are nonetheless limited by their form factor, often requiring bimanual setups even for simple reorientation tasks. Anthropomorphic hands are a more natural platform for dexterous robot learning -- closer to the human hand, and capable of learning from human video -- yet they remain hard to use in learning research: even where open and accessible hand hardware exists, the software for control, simulation, teleoperation, and retargeting is scattered in one-off code bases, and largely disconnected from the robot-learning ecosystem. In this work, we introduce the \orca~learning stack, an open-source research stack for dexterity as a first-class robot learning domain. Our \orca~stack unifies low-level control, simulation, teleoperation from a range of consumer platforms, and hand retargeting, behind a single interface, and integrates natively with popular robot-learning frameworks such as \lerobot, so dexterous hand researchers can leverage the same data, training, and evaluation pipelines used for non-dexterous robot learning. We demonstrate a complete end-to-end workflow, collecting expert demonstrations of an in-hand reorientation task by teleoperation with a consumer-grade VR headset, training an autonomous policy with \lerobot, and evaluating the learned policy in a fully reproducible and observable setup. We open-source the entire stack as a shared, reproducible foundation for dexterous-manipulation research.

Related papers

• LeRobot: An Open-Source Library for End-to-End Robot Learning [36.567938139028186]
  textttlerobot is an open-source library that integrates across the entire robot learning stack.<n>It supports efficient implementations for various state-of-the-art robot learning algorithms as well as a generalized asynchronous inference stack.
  arXiv  Detail & Related papers  (2026-02-26T09:59:50Z)
• Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations [52.29884993824894]
  Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community.<n>AINA enables learning multi-fingered policies from data collected by anyone, anywhere, and in any environment using Aria Gen 2 glasses.
  arXiv  Detail & Related papers  (2025-11-20T18:59:02Z)
• Ark: An Open-source Python-based Framework for Robot Learning [20.131425969692256]
  ARK is an open-source, Python-first robotics framework designed to close that gap.<n>ARK presents a Gym-style environment interface that allows users to collect data, preprocess it, and train policies.<n>ARK ships with reusable modules for control, SLAM, motion planning, system identification, and visualization.
  arXiv  Detail & Related papers  (2025-06-24T20:23:39Z)
• A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics [53.33976793493801]
  We organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We focus on practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging.
  arXiv  Detail & Related papers  (2024-11-08T17:20:47Z)
• Generalized Robot Learning Framework [10.03174544844559]
  We present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots.
  arXiv  Detail & Related papers  (2024-09-18T15:34:31Z)
• LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [72.81786007015471]
  We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem.<n>CNN processes localized perception; a graph neural network (GNN) facilitates robot communications.<n> Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
  arXiv  Detail & Related papers  (2024-01-10T00:08:00Z)
• Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback [27.223725464754853]
  GEAR enables robots to be placed in real-world environments and left to train autonomously without interruption. System streams robot experience to a web interface only requiring occasional asynchronous feedback from remote, crowdsourced, non-expert humans.
  arXiv  Detail & Related papers  (2023-10-31T16:43:56Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Evaluating Continual Learning on a Home Robot [30.620205237707342]
  We show how continual learning methods can be adapted for use on a real, low-cost home robot. We propose SANER, a method for continuously learning a library of skills, and ABIP, as the backbone to support it.
  arXiv  Detail & Related papers  (2023-06-04T17:14:49Z)
• 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)
• Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance [71.36749876465618]
  We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks. Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples. experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
  arXiv  Detail & Related papers  (2022-12-19T22:50:40Z)

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.