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pytorch_kinematics

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Robot kinematics implemented in pytorch

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- - python-package.yml
- - __init__.py
  - cfg.py
  - chain.py
  - frame.py
  - ik.py
  - jacobian.py
  - mjcf.py
  - sdf.py
  - urdf.py
- - cone.mtl
  - cone.obj
  - link_0.obj
  - link_1.obj
  - link_2.obj
  - link_3.obj
  - link_4.obj
  - link_5.obj
  - link_6.obj
  - link_7.obj
- - interbotix_black.png
  - README.md
  - wx250s.srdf
  - wx250s.urdf
- __init__.py
- ant.xml
- benchmark_ik.py
- gen_fk_perf.py
- gen_jac_perf.py
- hopper.xml
- humanoid.xml
- joint_limit_robot.urdf
- joint_no_limit_robot.urdf
- kuka_iiwa.urdf
- kuka_iiwa.xml
- link_0.stl
- link_1.stl
- link_2.stl
- link_3.stl
- link_4.stl
- link_5.stl
- link_6.stl
- link_7.stl
- prismatic_robot.urdf
- simple_arm.sdf
- simple_y_arm.urdf
- test_attributes.py
- test_ik_joint_limits.py
- test_inverse_kinematics.py
- test_jacobian.py
- test_kinematics.py
- test_menagerie.py
- test_rotation_conversions.py
- test_serial_chain_creation.py
- test_transform.py
- ur5.urdf
- val.xml
- viz_fk_perf.ipynb
.clang-format
.gitignore
CITATION.cff
LICENSE.txt
pyproject.toml
README.md

# Installation Guide

1. Get the code

git clone https://github.com/UM-ARM-Lab/pytorch_kinematics

Downloads the entire project code from GitHub to your computer.

cd pytorch_kinematics

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install pytorch-kinematics

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install pytorch-kinematics

Installs the package published on PyPI directly — no need to clone the source.

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# PyTorch Robot Kinematics
- Parallel and differentiable forward kinematics (FK), Jacobian calculation, and damped least squares inverse kinematics (IK)
- Load robot description from URDF, SDF, and MJCF formats 
- SDF queries batched across configurations and points via [pytorch-volumetric](https://github.com/UM-ARM-Lab/pytorch_volumetric)

# Installation
```shell
pip install pytorch-kinematics
```

For development, clone repository somewhere, then `pip3 install -e .` to install in editable mode.

## Reference
[![DOI](https://zenodo.org/badge/331721571.svg)](https://zenodo.org/badge/latestdoi/331721571)

If you use this package in your research, consider citing
```
@software{Zhong_PyTorch_Kinematics_2024,
author = {Zhong, Sheng and Power, Thomas and Gupta, Ashwin and Mitrano, Peter},
doi = {10.5281/zenodo.7700587},
month = feb,
title = {{PyTorch Kinematics}},
version = {v0.7.1},
year = {2024}
}
```

# Usage

See `tests` for code samples; some are also shown here.

## Loading Robots
```python
import pytorch_kinematics as pk

urdf = "widowx/wx250s.urdf"
# there are multiple natural end effector links so it's not a serial chain
chain = pk.build_chain_from_urdf(open(urdf, mode="rb").read())
# visualize the frames (the string is also returned)
chain.print_tree()
"""
base_link
└── shoulder_link
    └── upper_arm_link
        └── upper_forearm_link
            └── lower_forearm_link
                └── wrist_link
                    └── gripper_link
                        └── ee_arm_link
                            ├── gripper_prop_link
                            └── gripper_bar_link
                                └── fingers_link
                                    ├── left_finger_link
                                    ├── right_finger_link
                                    └── ee_gripper_link
"""

# extract a specific serial chain such as for inverse kinematics
serial_chain = pk.SerialChain(chain, "ee_gripper_link", "base_link")
serial_chain.print_tree()
"""
base_link
└── shoulder_link
    └── upper_arm_link
        └── upper_forearm_link
            └── lower_forearm_link
                └── wrist_link
                    └── gripper_link
                        └── ee_arm_link
                            └── gripper_bar_link
                                └── fingers_link
                                    └── ee_gripper_link
"""

# you can also extract a serial chain with a different root than the original chain
serial_chain = pk.SerialChain(chain, "ee_gripper_link", "gripper_link")
serial_chain.print_tree()
"""
 gripper_link
└── ee_arm_link
    └── gripper_bar_link
        └── fingers_link
            └── ee_gripper_link
"""
```

## Forward Kinematics (FK)
```python
import math
import pytorch_kinematics as pk

# load robot description from URDF and specify end effector link
chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7")
# prints out the (nested) tree of links
print(chain)
# prints out list of joint names
print(chain.get_joint_parameter_names())

# specify joint values (can do so in many forms)
th = [0.0, -math.pi / 4.0, 0.0, math.pi / 2.0, 0.0, math.pi / 4.0, 0.0]
# do forward kinematics and get transform objects; end_only=False gives a dictionary of transforms for all links
ret = chain.forward_kinematics(th, end_only=False)
# look up the transform for a specific link
tg = ret['lbr_iiwa_link_7']
# get transform matrix (1,4,4), then convert to separate position and unit quaternion
m = tg.get_matrix()
pos = m[:, :3, 3]
rot = pk.matrix_to_quaternion(m[:, :3, :3])
```

We can parallelize FK by passing in 2D joint values, and also use CUDA if available
```python
import torch
import pytorch_kinematics as pk

d = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float64

chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7")
chain = chain.to(dtype=dtype, device=d)

N = 1000
th_batch = torch.rand(N, len(chain.get_joint_parameter_names()), dtype=dtype, device=d)

# order of magnitudes faster when doing FK in parallel
# elapsed 0.008678913116455078s for N=1000 when parallel
# (N,4,4) transform matrix; only the one for the end effector is returned since end_only=True by default
tg_batch = chain.forward_kinematics(th_batch)

# elapsed 8.44686508178711s for N=1000 when serial
for i in range(N):
    tg = chain.forward_kinematics(th_batch[i])
```

We can compute gradients through the FK
```python
import torch
import math
import pytorch_kinematics as pk

chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7")

# require gradient through the input joint values
th = torch.tensor([0.0, -math.pi / 4.0, 0.0, math.pi / 2.0, 0.0, math.pi / 4.0, 0.0], requires_grad=True)
tg = chain.forward_kinematics(th)
m = tg.get_matrix()
pos = m[:, :3, 3]
pos.norm().backward()
# now th.grad is populated
```

### Analytical FK Backward

`forward_kinematics_tensor` uses an **analytical geometric Jacobian** for the backward pass by default.
Instead of replaying all the forward ops through autograd, it computes `d(transform)/d(joint_angles)`
directly from joint axes and the kinematic tree structure. This is ~9x faster than standard autograd
on GPU for large batch sizes.

```python
import torch
import pytorch_kinematics as pk

chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7")

th = torch.randn(1000, 7, requires_grad=True)
T_all = chain.forward_kinematics_tensor(th)  # (num_frames, 1000, 4, 4)

# backward uses the analytical Jacobian automatically
loss = T_all.sum()
loss.backward()  # th.grad is populated, ~9x faster than autograd on GPU
```

The analytical backward is compatible with `torch.compile(fullgraph=True)` — all arguments crossing the
autograd boundary are plain tensors.

**Escape hatch**: set `analytical_grad=False` when you need:
- **Higher-order gradients** (`create_graph=True` / double backward)
- **Gradients w.r.t. chain parameters** (e.g. differentiating through link offsets for calibration)

```python
# Standard autograd — supports create_graph=True and parameter gradients
T_all = chain.forward_kinematics_tensor(th, analytical_grad=False)
```

We can load SDF and MJCF descriptions too, and pass in joint values via a dictionary (unspecified joints get th=0) for non-serial chains
```python
import math
import torch
import pytorch_kinematics as pk

chain = pk.build_chain_from_sdf(open("simple_arm.sdf").read())
ret = chain.forward_kinematics({'arm_elbow_pan_joint': math.pi / 2.0, 'arm_wrist_lift_joint': -0.5})
# recall that we specify joint values and get link transforms
tg = ret['arm_wrist_roll']

# can also do this in parallel
N = 100
ret = chain.forward_kinematics({'arm_elbow_pan_joint': torch.rand(N, 1), 'arm_wrist_lift_joint': torch.rand(N, 1)})
# (N, 4, 4) transform object
tg = ret['arm_wrist_roll']

# building the robot from a MJCF file
chain = pk.build_chain_from_mjcf(open("ant.xml").read())
print(chain)
print(chain.get_joint_parameter_names())
th = {'hip_1': 1.0, 'ankle_1': 1}
ret = chain.forward_kinematics(th)

chain = pk.build_chain_from_mjcf(open("humanoid.xml").read())
print(chain)
print(chain.get_joint_parameter_names())
th = {'left_knee': 0.0, 'right_knee': 0.0}
ret = chain.forward_kinematics(th)
```

## torch.compile Support

The FK computation can be compiled with `torch.compile(fullgraph=True)` for significant speedups,
especially for applications that call FK thousands of times (e.g. inverse kinematics, trajectory optimization).

Use `forward_kinematics_tensor`, a compile-friendly variant that accepts and returns raw tensors
instead of dicts and `Transform3d` objects. The analytical FK backward (see above) works under
`torch.compile` — both forward and backward are fully traced:

```python
import torch
import pytorch_kinematics as pk

chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7")

# compile the FK kernel (one-time cost)
compiled_fk = torch.compile(chain.forward_kinematics_tensor, fullgraph=True, dynamic=True)

# input: (B, n_joints) tensor
th = torch.randn(1000, 7)

# output: (num_frames, B, 4, 4) tensor of homogeneous transforms for all frames
all_transforms = compiled_fk(th)

# index by frame: use chain.frame_to_idx to look up the index for a frame name
ee_idx = chain.frame_to_idx['lbr_iiwa_link_7']
ee_transform = all_transforms[ee_idx]  # (B, 4, 4)
```

Typical speedups on CPU (Kuka IIWA 7-DOF):

| Batch size | `forward_kinematics` | Compiled `forward_kinematics_tensor` | Speedup |
|---:|---:|---:|---:|
| 1 | 0.21 ms | 0.04 ms | **4.7x** |
| 64 | 0.26 ms | 0.08 ms | **3.5x** |
| 1024 | 1.13 ms | 0.51 ms | **2.2x** |

Speedups are larger for complex robots with more frames (e.g. 6x+ for 49-frame robots at batch=1).
GPU speedups from CUDA graph capture provide additional gains at large batch sizes.

The standard `forward_kinematics` method (returning `Dict[str, Transform3d]`) also benefits from the
refactored internals without any code changes. It is 2-6x faster at small batch sizes compared to v0.7.

The rotation conversion functions (`matrix_to_quaternion`, `quaternion_to_axis_angle`,
`axis_angle_to_quaternion`, etc.) are also compatible with `torch.compile(fullgraph=True)` and can
be compiled standalone or as part of a larger compiled graph:

```python
import torch
import pytorch_kinematics as pk

# compile a rotation conversion function
compiled_m2q = torch.compile(pk.matrix_to_quaternion, fullgraph=True)
R = torch.randn(100, 3, 3)
q = compiled_m2q(R)
```

## Jacobian calculation
The Jacobian (in the kinematics context) is a matrix describing how the end effector changes with respect to joint value changes
(where ![dx](https://latex.codecogs.com/png.latex?%5Cinline%20%5Cdot%7Bx%7D) is the twist, or stacked velocity and angular velocity):
![jacobian](https://latex.codecogs.com/png.latex?%5Cinline%20%5Cdot%7Bx%7D%3DJ%5Cdot%7Bq%7D)

For `SerialChain` we provide a differentiable and parallelizable method for computing the Jacobian with respect to the base frame.
```python
import math
import torch
import pytorch_kinematics as pk

# can convert Chain to SerialChain by choosing end effector frame
chain = pk.build_chain_from_sdf(open("simple_arm.sdf").read())
# print(chain) to see the available links for use as end effector
# note that any link can be chosen; it doesn't have to be a link with no children
chain = pk.SerialChain(chain, "arm_wrist_roll_frame")

chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7")
th = torch.tensor([0.0, -math.pi / 4.0, 0.0, math.pi / 2.0, 0.0, math.pi / 4.0, 0.0])
# (1,6,7) tensor, with 7 corresponding to the DOF of the robot
J = chain.jacobian(th)

# get Jacobian in parallel and use CUDA if available
N = 1000
d = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float64

chain = chain.to(dtype=dtype, device=d)
# Jacobian calculation is differentiable
th = torch.rand(N, 7, dtype=dtype, device=d, requires_grad=True)
# (N,6,7)
J = chain.jacobian(th)

# can get Jacobian at a point offset from the end effector (location is specified in EE link frame)
# by default location is at the origin of the EE frame
loc = torch.rand(N, 3, dtype=dtype, device=d)
J = chain.jacobian(th, locations=loc)
```

Like FK, the Jacobian has a `torch.compile`-compatible variant `jacobian_tensor`:

```python
chain = pk.build_serial_chain_from_urdf(open("kuka_iiwa.urdf").read(), "lbr_iiwa_link_7")

# compile the Jacobian kernel (one-time cost)
compiled_jac = torch.compile(chain.jacobian_tensor, fullgraph=True)

# input: (B, n_joints) tensor; output: (B, 6, n_joints) Jacobian
th = torch.randn(100, 7)
J = compiled_jac(th)
```

For maximum performance in tight loops (e.g. IK), pass `mode='max-autotune'` to `torch.compile`
for additional kernel tuning at the cost of longer compilation.

Typical speedups on CPU (Kuka IIWA 7-DOF, vs the old v0.7 `calc_jacobian`):

| Batch size | Old `calc_jacobian` | New `jacobian` (eager) | Compiled `jacobian_tensor` | Speedup (compiled vs old) |
|---:|---:|---:|---:|---:|
| 1 | 0.63 ms | 0.12 ms | 0.03 ms | **21x** |
| 10 | 0.79 ms | 0.15 ms | 0.05 ms | **15x** |
| 100 | 0.75 ms | 0.30 ms | 0.09 ms | **8x** |
| 1,000 | 1.67 ms | 1.23 ms | 0.53 ms | **3x** |
| 10,000 | 7.93 ms | 7.01 ms | 4.91 ms | **1.6x** |
| 100,000 | 59.42 ms | 64.58 ms | 46.46 ms | **1.3x** |

At small-to-medium batch sizes (the typical IK regime), the new eager implementation is 2-5x faster
due to vectorized computation replacing the per-frame Python loop.
`torch.compile` provides an additional 2-4x on top of that, and is faster than the old implementation
at every batch size.

The Jacobian can be used to do inverse kinematics. See [IK survey](https://www.math.ucsd.edu/~sbuss/ResearchWeb/ikmethods/iksurvey.pdf)
for a survey of ways to do so. Note that IK may be better performed through other means (but doing it through the Jacobian can give an end-to-end differentiable method).

## Inverse Kinematics (IK)
Inverse kinematics is available via damped least squares (iterative steps with Jacobian pseudo-inverse damped to avoid oscillation near singularlities). 
Compared to other IK libraries, these are the typical advantages over them:
- not ROS dependent (many IK libraries need the robot description on the ROS parameter server)
- batched in both goal specification and retries from different starting configurations
- goal orientation in addition to goal position

![IK](https://i.imgur.com/QgaUME9.gif)

See `tests/test_inverse_kinematics.py` for usage, but generally what you need is below:
```python
full_urdf = os.path.join(search_path, urdf)
chain = pk.build_serial_chain_from_urdf(open(full_urdf).read(), "lbr_iiwa_link_7")

# goals are specified as Transform3d poses in the **robot frame**
# so if you have the goals specified in the world frame, you also need the robot frame in the world frame
pos = torch.tensor([0.0, 0.0, 0.0], device=device)
rot = torch.tensor([0.0, 0.0, 0.0], device=device)
rob_tf = pk.Transform3d(pos=pos, rot=rot, device=device)

# specify goals as Transform3d poses in world frame
goal_in_world_frame_tf = ...
# convert to robot frame (skip if you have it specified in robot frame already, or if world = robot frame)
goal_in_rob_frame_tf = rob_tf.inverse().compose(goal_tf)

# get robot joint limits
lim = torch.tensor(chain.get_joint_limits(), device=device)

# create the IK object
# see the constructor for more options and their explanations, such as convergence tolerances
ik = pk.PseudoInverseIK(chain, max_iterations=30, num_retries=10,
                        joint_limits=lim.T,
                        early_stopping_any_converged=True,
                        early_stopping_no_improvement="all",
                        debug=False,
                        lr=0.2)
# solve IK
sol = ik.solve(goal_in_rob_frame_tf)
# num goals x num retries x DOF tensor of joint angles; if not converged, best solution found so far
print(sol.solutions)
# num goals x num retries can check for the convergence of each run
print(sol.converged)
# num goals x num retries can look at errors directly
print(sol.err_pos)
print(sol.err_rot)
```

### Compiled IK

For workloads that solve IK repeatedly (e.g. in a planning loop), pass `use_compile=True` to compile the
FK, Jacobian, and damped least squares kernels inside the IK loop with `torch.compile`.
The outer IK loop with its data-dependent convergence checks remains in eager mode.

```python
ik = pk.PseudoInverseIK(chain, max_iterations=30, num_retries=10,
                         joint_limits=lim.T,
                         early_stopping_any_converged=True,
                         early_stopping_no_improvement="all",
                         lr=0.2,
                         use_compile=True)  # compile inner kernels

# first solve() incurs a one-time compilation cost; subsequent calls are faster
sol = ik.solve(goal_in_rob_frame_tf)
```

Typical speedups on CPU (Kuka IIWA 7-DOF, 30 iterations):

| Retries | Eager (v0.7) | Eager (new) | Compiled | Speedup (compiled vs v0.7) |
|---:|---:|---:|---:|---:|
| 10 | 25.6 ms | 23.3 ms | 11.8 ms | **2.2x** |
| 50 | 31.5 ms | 29.0 ms | 15.8 ms | **2.0x** |

Note: `use_compile=True` requires PyTorch 2.0+. You cannot wrap `ik.solve()` directly with
`torch.compile` due to data-dependent control flow in the outer loop — use this flag instead.

## SDF Queries
See [pytorch-volumetric](https://github.com/UM-ARM-Lab/pytorch_volumetric) for the latest details, some instructions are pasted here:

For many applications such as collision checking, it is useful to have the
SDF of a multi-link robot in certain configurations.
First, we create the robot model (loaded from URDF, SDF, MJCF, ...) with
[pytorch kinematics](https://github.com/UM-ARM-Lab/pytorch_kinematics).
For example, we will be using the KUKA 7 DOF arm model from pybullet data

```python
import os
import torch
import pybullet_data
import pytorch_kinematics as pk
import pytorch_volumetric as pv

urdf = "kuka_iiwa/model.urdf"
search_path = pybullet_data.getDataPath()
full_urdf = os.path.join(search_path, urdf)
chain = pk.build_serial_chain_from_urdf(open(full_urdf).read(), "lbr_iiwa_link_7")
d = "cuda" if torch.cuda.is_available() else "cpu"

chain = chain.to(device=d)
# paths to the link meshes are specified with their relative path inside the URDF
# we need to give them the path prefix as we need their absolute path to load
s = pv.RobotSDF(chain, path_prefix=os.path.join(search_path, "kuka_iiwa"))
```

By default, each link will have a `MeshSDF`. To instead use `CachedSDF` for faster queries

```python
s = pv.RobotSDF(chain, path_prefix=os.path.join(search_path, "kuka_iiwa"),
                link_sdf_cls=pv.cache_link_sdf_factory(resolution=0.02, padding=1.0, device=d))
```

Which when the `y=0.02` SDF slice is visualized:

![sdf slice](https://i.imgur.com/Putw72A.png)

With surface points corresponding to:

![wireframe](https://i.imgur.com/L3atG9h.png)
![solid](https://i.imgur.com/XiAks7a.png)

Queries on this SDF is dependent on the joint configurations (by default all zero).
**Queries are batched across configurations and query points**. For example, we have a batch of
joint configurations to query

```python
th = torch.tensor([0.0, -math.pi / 4.0, 0.0, math.pi / 2.0, 0.0, math.pi / 4.0, 0.0], device=d)
N = 200
th_perturbation = torch.randn(N - 1, 7, device=d) * 0.1
# N x 7 joint values
th = torch.cat((th.view(1, -1), th_perturbation + th))
```

And also a batch of points to query (same points for each configuration):

```python
y = 0.02
query_range = np.array([
    [-1, 0.5],
    [y, y],
    [-0.2, 0.8],
])
# M x 3 points
coords, pts = pv.get_coordinates_and_points_in_grid(0.01, query_range, device=s.device)
```

We set the batch of joint configurations and query:

```python
s.set_joint_configuration(th)
# N x M SDF value
# N x M x 3 SDF gradient
sdf_val, sdf_grad = s(pts)
```

# Credits
- `pytorch_kinematics/transforms` is extracted from [pytorch3d](https://github.com/facebookresearch/pytorch3d) with minor extensions.
This was done instead of including `pytorch3d` as a dependency because it is hard to install and most of its code is unrelated.
  An important difference is that we use left hand multiplied transforms as is convention in robotics (T * pt) instead of their
  right hand multiplied transforms.
- `pytorch_kinematics/urdf_parser_py`, and `pytorch_kinematics/mjcf_parser` is extracted from [kinpy](https://github.com/neka-nat/kinpy), as well as the FK logic.
This repository ports the logic to pytorch, parallelizes it, and provides some extensions.