kmk 0.1.0

Keypoint-based multi-fingered hand kinematics and configuration tools.

kmk

kmk is a utility repo for authoring and consuming keypoint-based multi-fingered hand kinematics data.

It has two main jobs:

• a staged GUI wizard that builds a hand config YAML
• runtime loaders and torch kinematics utilities that read that YAML

What This Repo Provides

• gripper_config_wizard
  • staged authoring flow for:
    • global hand config
    • per-link contact anchors
    • grasp templates
    • final preview/confirmation
• HandInfo
  • read-only runtime wrapper around the authored YAML
• PointedHandInfo
  • HandInfo plus precomputed surface/contact point samples
• HandKinematics
  • torch forward kinematics for batched joint vectors and link-local point sets

User Workflow

1. Prepare a gripper root directory containing at least a URDF.
2. Run the wizard.
3. Fill the global stage.
   • palm pose
   • palm points delta
   • global q_open
   • extra collision ignore pairs
4. Fill the keypoint stage.
   • one contact anchor per link
   • point, radius, tags
5. Fill the grasp template stage.
   • q_open, q_close
   • grasp_target_point
   • active contact anchors
6. Check the final preview and press Confirmed.
7. Use the saved YAML through HandInfo, PointedHandInfo, or HandKinematics.

Install

uv sync

Wizard

Create a new config:

uv run gripper_config_wizard --gripper-root /path/to/gripper_root

Edit an existing config:

uv run gripper_config_wizard \
  --gripper-root /path/to/gripper_root \
  --from-config existing.yaml

Notes:

• --from-config relative paths are resolved from gripper_root.
• urdf_path and xml_path stored in YAML are typically relative paths.

Visualization

Inspect a saved config and sampled points:

uv run python visualize/pointed_hand_info.py --config-path /path/to/hand.yaml

Inspect batched torch kinematics:

uv run python visualize/hand_kinematics_batch.py --config-path /path/to/hand.yaml

Runtime API

HandInfo

from kmk import HandInfo

hand = HandInfo.from_config("hand.yaml")
q_open = hand.get_q_open()
q_close = hand.get_q_close("finger4_shallow")
palm_pose = hand.get_palm_pose()
anchor = hand.get_contact_anchor_by_link("right_1thumb_distal")
anchors = hand.get_contact_anchor_by_template("finger4_shallow")
target = hand.get_grasp_target_point("finger4_shallow")

Main API:

• HandInfo.from_config(config_path)
• joint_order
• template_names
• contact_anchor_links
• get_q_open(template="global")
• get_q_close(template_name)
• get_palm_pose()
• get_contact_anchor_by_link(link_name)
• get_contact_anchor_by_tag(includes=(), excludes=())
• get_contact_anchor_by_template(template_name)
• get_grasp_target_point(template_name)

PointedHandInfo

from kmk import PointedHandInfo

hand = PointedHandInfo.from_config("hand.yaml", seed=0)
surface_points = hand.surface_points
contact_points = hand.get_contact_points("finger4_shallow")
keypoints = hand.get_keypoints("finger4_shallow", palm_aligned_points=True)

Main additions:

• surface_points
• contact_points
• get_contact_points(template_name=None)
• get_keypoints(template_name=None, palm_aligned_points=True, palm_points_delta=...)

HandKinematics

import torch
from kmk import HandInfo, HandKinematics

hand = HandInfo.from_config("hand.yaml")
kin = HandKinematics(hand).to(device="cpu")

q = torch.zeros(16, len(hand.joint_order))
fk = kin.forward_kinematics(q)
world_points = kin.transform_link_points(
    q,
    {"right_1thumb_distal": torch.tensor([[0.0, 0.0, 0.02]])},
)

Main API:

• HandKinematics(hand_info_or_config_path)
• joint_order
• link_names
• dof
• forward_kinematics(q)
• transform_link_points(q, points_by_link)
• get_palm_pose(batch_shape=None)

Config Schema

The detailed spec lives in SPEC.md.