almond_axol.kinematics

Bimanual inverse kinematics using pyroki and JAX/jaxls. Loads the bundled URDF, builds a collision model, and JIT-compiles the solver during __init__ — the first call takes a few seconds; subsequent calls are fast.

The XLA compile is cached to disk (~/.almond/jax-cache), so that cost is paid once per machine, not on every process start — a fresh axol teleop / serve boots straight into the fast path instead of recompiling. The cache key covers the solver graph plus the jax/jaxlib versions and backend, so upgrading JAX or changing the solver simply recompiles and refreshes the cache (stale entries are never reused). Only the XLA backend compile is skipped; Python-side tracing/lowering still runs each session. Set JAX_COMPILATION_CACHE_DIR to override the location.

from almond_axol.kinematics import KinematicsSolver, KinematicsConfig

import numpy as np
from almond_axol.kinematics import KinematicsSolver, KinematicsConfig

solver = KinematicsSolver(KinematicsConfig(pos_weight=100.0))
q = np.zeros(solver.num_joints, dtype=np.float32)

# Forward kinematics → (left_SE3, right_SE3) as jaxlie.SE3
left_se3, right_se3 = solver.fk(q)

# Inverse kinematics → new joint array
pos = np.array([0.3, 0.2, 0.4], dtype=np.float32)
rot = np.eye(3, dtype=np.float32)
q = solver.ik(q, left_pose=(pos, rot))

`KinematicsSolver` interface

Member	Description
`num_joints`	Total actuated joints across both arms
`joint_names`	Joint name strings (left arm then right)
`left_indices` / `right_indices`	Indices into the full `q` array for each arm
`fk(q)`	Forward kinematics → `(left_SE3, right_SE3)`
`ik(q, left_pose, right_pose, left_elbow_pos, right_elbow_pos)`	IK; `pose` is `(pos_3, rot_3x3)`; elbow hints are optional `(3,)` arrays
`set_posture_pose(q)`	Set the null-space attractor (home pose for joint drift prevention)

`KinematicsConfig` fields

Field	Default	Description
`pos_weight`	`50.0`	End-effector position tracking weight
`ori_weight`	`10.0`	End-effector orientation tracking weight
`elbow_weight`	`5.0`	Elbow hint tracking weight
`rest_weight`	`7.5`	Per-step damping; penalises deviation from `q_current`
`posture_weight`	`5.0`	Persistent attractor to home pose (prevents null-space drift)
`manipulability_weight`	`0.05`	Reward for configurations with high manipulability
`limit_weight`	`75.0`	Joint limit penalty weight
`self_collision_margin`	`0.1` m	Minimum clearance between collision bodies
`self_collision_weight`	`75.0`	Self-collision penalty weight
`max_iterations`	`8`	Solver iterations per `ik()` call
`cost_tolerance`	`1e-2`	Convergence tolerance
`max_joint_delta`	`~0.035` rad	Maximum joint change per `ik()` call
`max_reach`	`0.8` m	EE target clamped to this distance from shoulder

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almond_axol.kinematics

`KinematicsSolver` interface

`KinematicsConfig` fields

​KinematicsSolver interface

​KinematicsConfig fields

`KinematicsSolver` interface

`KinematicsConfig` fields