VLA Robot Candidates for Flower Trimming and Bouquet Making
Compares purchasable robot candidates for real flower trimming and bouquet-making tasks across LeRobot support, bimanual manipulation, reach, dexterity, durability, price, open-source status, and customization.
Summary
Flower trimming and bouquet making are harder than simple pick-and-place. The task includes aligning stem lengths, removing leaves, orienting flowers, holding a bundle with one hand while manipulating wrap or string with the other, and potentially using tools. The main buying criteria are bimanual manipulation, workspace reach, swappable grippers/hands, data collection pipeline, and customization headroom.
Interesting LeRobot-supported humanoid candidate, but walking and safety complexity is high for table flower work.
The practical path is to first validate the LeRobot record/train/inference loop with SO-101 or LeKiwi, then select OpenArm, ALOHA 2, Galaxea R1 Pro, or Reachy 2 as the main experiment platform.
Source Basis for This Update
The shortlist is not based on manufacturer copy alone. It is cross-checked against these sources:
Source
How it was used
Hugging Face LeRobot official docs
Filter robots that are easier to put into record/train/inference workflows
Humanoid Guide / HumanoidSpecs hands directory
Broad recent scan of hands and grippers
Robot Manipulation hardware list
Wider research-manipulation hardware context
Manufacturer/lab official docs
Spec and integration checks for WUJI, ORCA, SharpaWave, and North
Papers and preprints
Evidence for open-source reproducibility, especially ORCA
Evaluation Criteria
Criterion
Why it matters
Purchasability
Demo-only robots are excluded because the goal is to train and deploy a custom VLA.
LeRobot support
It shortens the path to recording, training, Hub sharing, and policy deployment.
Bimanual manipulation
Bouquet work often needs one hand to hold while the other aligns, ties, wraps, or uses tools.
Reach/payload
The robot must cover flowers, scissors, wrapping paper, vases, and trays on a tabletop.
Dexterity
Contact surface, force control, and end-effector options matter for avoiding damage.
Durability
Repeated data collection stresses motors, gears, frames, and cables.
Open-source/customization
Cameras, custom grippers, safety interlocks, and VLA inference need direct integration.
Price
Cameras, compute, grippers, safety cells, and spare parts add to the robot cost.
The pragmatic first test is not an expensive dexterous hand. Mount OnRobot Soft Gripper or Robotiq 2F with replaceable silicone pads on OpenArm/ALOHA/xArm and measure flower damage. Move to LEAP Hand, WUJI Hand, ORCA Hand, SharpaWave, Ability Hand, or Allegro Hand only if in-hand rotation, tactile force caps, or fine orientation becomes necessary.
Reference platform for SharpaWave hands on a wheeled full-body robot. Keep it as a watch candidate until SDK, pricing, lead time, and safety access are public enough to evaluate.
Buying Scenarios
Budget-Constrained
Validate LeRobot record/train/inference with SO-101 or LeKiwi.
Source or build bimanual OpenArm.
Add silicone pads and simple force/pressure sensing to the base gripper.
Collect data in stages: single flower pick, stem alignment, then simple tying.
Research-Reproduction
Buy or build ALOHA 2 / Trossen ALOHA.
Reproduce ACT/ALOHA baselines.
Fine-tune OpenVLA, SmolVLA, or pi0-style policies.
Define bouquet making as an ALOHA-style benchmark.
Time-Saving
Request quotes for Galaxea R1 Pro or Reachy 2.
Collect human demonstrations through VR/isomorphic teleoperation.
Connect LeRobot or a custom ROS2 data conversion pipeline.
Validate VLA inference inside a safety cell.
Archive Version
The longer live-content research report is also available in the Archive version.