Dobb·E 
Dobb·E is an open-source research framework for learning household robotic manipulation from demonstrations, with published hardware, datasets, models, paper and code. Researchers should follow hardware and environment safety procedures, inspect dataset consent and licenses, reproduce benchmarks, test failure modes in controlled spaces, supervise every robot action, and never deploy around people without appropriate safeguards.
Starting price Free open source
Tool Information
Dobb·E is an open-source research framework for learning household robotic manipulation from demonstrations, with published hardware, datasets, models, paper and code. Researchers should follow hardware and environment safety procedures, inspect dataset consent and licenses, reproduce benchmarks, test failure modes in controlled spaces, supervise every robot action, and never deploy around people without appropriate safeguards.
Begin with authorized, minimal, non-sensitive inputs. Configure access, privacy, retention, visibility, model, citations, quality, disclosure, integration, export, moderation, safety, and spending controls. Compare results with authoritative sources, correct errors and artifacts, test the complete workflow, and retain accountable human approval before publication, outreach, travel booking, financial action, health use, or deployment.
The research code, models and project materials are free and open source. Hardware, fabrication, sensors, compute, storage, maintenance, insurance, safety testing, and deployment create separate costs.
AI output can be inaccurate, speculative, biased, derivative, unsafe, technically flawed, or misleading. Review consent, copyright, commercial rights, training, retention, renewal, refunds, security, and platform rules. Legal, credit, financial, medical, nutrition, research, robotics, and customer-service outputs require qualified review.
F.A.Q (3)
Pros and Cons
Pros
- Provides an open-source framework for learning household robot manipulation
- Publishes its software stack and trained models
- Publishes hardware designs for its demonstration-collection tool
- Uses an inexpensive $25 reacher-grabber as a core component of the Stick
- Provides downloadable 3D-printing files and a build guide
- Collects demonstrations with an iPhone-based tool
- Can learn from five minutes of a user's task demonstrations
- Adapts its pretrained representation in about fifteen additional minutes in the reported setup
- Was tested across 109 household tasks
- Was evaluated in 10 New York-area homes
- Reports an 81% task-success rate in those experiments
- The training dataset spans 22 homes and 216 environments
- Publishes RGB and RGB-D datasets with action annotations
- Provides 5;620 trajectories and about 1.5 million frames
- Offers the HPR model through Hugging Face and TIMM
- Uses an MIT license for the source code
Cons
- Dobb·E is a research framework rather than a ready-to-buy domestic robot service
- Its reported 81% success rate still implies failures in roughly one out of five tested attempts
- Results came from only 10 test homes in one geographic area
- The study covers 109 selected tasks rather than unrestricted household autonomy
- Deployment depends on a separate Stretch mobile robot and compatible hardware
- Five minutes of demonstration plus fifteen minutes of adaptation is required for each new task in the reported workflow
- Non-expert demonstration quality can materially affect results
- Strong shadows and other ordinary home conditions caused challenges
- Robot manipulation failures can spill; break; damage; or mishandle household objects
- Operation near children; pets; stairs; heat; sharp objects; or people requires strong safety controls
- The 77 GB RGB-D dataset is large to download and process
- Home video datasets can reveal private layouts; possessions; and resident behavior
- The dataset reflects a limited sample and may not generalize to other homes; cultures; or objects
- Reproducing the research requires robotics; machine-learning; calibration; and fabrication expertise
- Open-source availability does not include a warranty; support SLA; or safety certification
- The 2023 research results should not be interpreted as current consumer-product performance
Reviews
You must be logged in to submit a review.
No reviews yet. Be the first to review!
Overview
Before you subscribe
- Check the currency, billing period, automatic renewal, usage limits and seats at checkout — prices and promotions change.
- App-store pricing can differ from website pricing by country, device and tax.
- Share only authorised data, and read the provider's encryption, retention and model-training terms first.
- AI output can be incomplete or wrong. Verify facts, citations, calculations and code before you publish or ship.
- Commercial usage rights vary by plan, provider terms and jurisdiction.
- None of these tools replaces a qualified professional for medical, legal or financial decisions.
Promote
Share this Tool
Similar Tools
Figure develops general-purpose humanoid robots and embodied AI systems for home and workplace tasks. Deployers should conduct rigorous physical-safety testing, limit permissions and environments, protect sensor data, plan emergency stops and supervision, monitor failures, and follow robotics and workplace regulations.
August 20, 2026
Custom pricing
Industrial Render AI turns product-design sketches, concepts, and references into realistic industrial-design renders and visualizations. Designers should use authorized materials, verify geometry, scale, materials, mechanisms, manufacturability, and safety, preserve engineering files, and treat generated images as concepts rather than validated specifications.
August 20, 2026
Free + paid plans
Jacobi Robotics provides robot motion planning, programming, simulation, or industrial automation tools. Engineers should validate models and trajectories in simulation and controlled environments, enforce safety limits, protect operational systems, document assumptions, and retain qualified human supervision.
August 20, 2026
Custom pricing