ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation
- URL: http://arxiv.org/abs/2405.02292v1
- Date: Wed, 7 Feb 2024 23:58:10 GMT
- Title: ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation
- Authors: ALOHA 2 Team, Jorge Aldaco, Travis Armstrong, Robert Baruch, Jeff Bingham, Sanky Chan, Kenneth Draper, Debidatta Dwibedi, Chelsea Finn, Pete Florence, Spencer Goodrich, Wayne Gramlich, Torr Hage, Alexander Herzog, Jonathan Hoech, Thinh Nguyen, Ian Storz, Baruch Tabanpour, Leila Takayama, Jonathan Tompson, Ayzaan Wahid, Ted Wahrburg, Sichun Xu, Sergey Yaroshenko, Kevin Zakka, Tony Z. Zhao,
- Abstract summary: ALOHA 2 is an enhanced version of ALOHA that has greater performance, ergonomics, and robustness compared to the original design.
We open source all hardware designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of ALOHA 2 with system identification.
- Score: 67.94622443802479
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robustness, and the ease of teleoperation. We introduce ALOHA 2, an enhanced version of ALOHA that has greater performance, ergonomics, and robustness compared to the original design. To accelerate research in large-scale bimanual manipulation, we open source all hardware designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of ALOHA 2 with system identification. See the project website at aloha-2.github.io.
Related papers
- SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks [50.97089872043121]
We propose SAM2-UNeXT, an advanced framework that builds upon the core principles of SAM2-UNet.<n>We extend the representational capacity of SAM2 through the integration of an auxiliary DINOv2 encoder.<n>Our approach enables more accurate segmentation with a simple architecture, relaxing the need for complex decoder designs.
arXiv Detail & Related papers (2025-08-05T15:36:13Z) - SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models [11.670056503731905]
We introduce SEKI, a novel large language model (LLM)-based neural architecture search (NAS) method.
Inspired by the chain-of-thought (CoT) paradigm in modern LLMs, SEKI operates in two key stages: self-evolution and knowledge distillation.
arXiv Detail & Related papers (2025-02-27T09:17:49Z) - The Ingredients for Robotic Diffusion Transformers [47.61690903645525]
We identify, study and improve key architectural design decisions for high-capacity diffusion transformer policies.
The resulting models can efficiently solve diverse tasks on multiple robot embodiments.
We find that our policies show improved scaling performance when trained on 10 hours of highly multi-modal, language annotated ALOHA demonstration data.
arXiv Detail & Related papers (2024-10-14T02:02:54Z) - Learning Visuotactile Skills with Two Multifingered Hands [80.99370364907278]
We explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data.
Our results mark a promising step forward in bimanual multifingered manipulation from visuotactile data.
arXiv Detail & Related papers (2024-04-25T17:59:41Z) - ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models [51.35570730554632]
ESPnet-SPK is a toolkit for training speaker embedding extractors.
We provide several models, ranging from x-vector to recent SKA-TDNN.
We also aspire to bridge developed models with other domains.
arXiv Detail & Related papers (2024-01-30T18:18:27Z) - Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost
Whole-Body Teleoperation [59.21899709023333]
We develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control.
Mobile ALOHA is a low-cost and whole-body teleoperation system for data collection.
Co-training can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously complete complex mobile manipulation tasks.
arXiv Detail & Related papers (2024-01-04T07:55:53Z) - Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL
Designs [15.67829950106923]
We introduce an innovative open-source framework that translates RTL designs into graph representation foundations.
The Verilog-to-PyG (V2PYG) framework is compatible with the open-source Electronic Design Automation (EDA) toolchain OpenROAD.
We will present novel RTL data augmentation methods that enable functional equivalent design augmentation for the construction of an extensive graph-based RTL design database.
arXiv Detail & Related papers (2023-11-09T20:11:40Z) - Sat2lod2: A Software For Automated Lod-2 Modeling From Satellite-Derived
Orthophoto And Digital Surface Model [7.219077740523683]
We describe an open-source tool, called SAT2LOD2, built on a minorly modified version of our recently published work.
SAT2LoD2 is a fully open-source and GUI (Graphics User Interface) based software, coded in Python.
arXiv Detail & Related papers (2022-04-08T15:49:35Z) - A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware [0.0]
This work presents the software aspects of the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling.
We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation.
The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development.
arXiv Detail & Related papers (2022-03-21T16:30:18Z) - HW2VEC: A Graph Learning Tool for Automating Hardware Security [4.188344897982036]
We propose HW2VEC, an open-source graph learning tool for hardware security applications.
We show that HW2VEC can achieve state-of-the-art performance on two hardware security-related tasks: Hardware Trojan Detection and Intellectual Property Piracy Detection.
arXiv Detail & Related papers (2021-07-26T17:03:51Z) - NAS-Count: Counting-by-Density with Neural Architecture Search [74.92941571724525]
We automate the design of counting models with Neural Architecture Search (NAS)
We introduce an end-to-end searched encoder-decoder architecture, Automatic Multi-Scale Network (AMSNet)
arXiv Detail & Related papers (2020-02-29T09:18:17Z)
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.