J-PARSE: Jacobian-based Projection Algorithm for Resolving Singularities Effectively in Inverse Kinematic Control of Serial Manipulators
cs.RO
Submitted: 2025-05-01
Updated: 2026-09-22
Comments: 21 pages, 13 figures. v1: Fig. 1 replaced with faster-loading version. v2: Website at https://jparse-manip.github.io/. v3: Proofs revised and new material added. v4: Proofs further revised and more new material added. v5: Figures improved; explanations expanded
Code: https://github.com/moveit/moveit
Project page: https://jparse-manip.github.io
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: J-PARSE is an algorithm for smooth first-order inverse kinematic control of a serial manipulator near kinematic singularities.
Terminology
Abstract
J-PARSE is an algorithm for smooth first-order inverse kinematic control of a serial manipulator near kinematic singularities. The commanded end-effector velocity is interpreted component-wise, according to the available mobility in each dimension of the task space. First, a substitute ''Safety'' Jacobian matrix is created, keeping the aspect ratio of the manipulability ellipsoid above a threshold value. The desired velocity is then projected onto non-singular and singular directions, and the latter projection scaled down by a factor informed by the altered mobility. A right-inverse of the non-singular Safety Jacobian is applied to the modified command. In the absence of joint limits and collisions, this ensures safe transition into and out of low-mobility configurations, guaranteeing locally stable reaching behavior towards target poses within, on the boundary of, and outside the workspace. The behavior is further guaranteed to be locally asymptotically stable if the starting and target configurations are not exactly singular, even if they are nearly singular. Velocity control with J-PARSE is benchmarked against approaches from the literature, illustrating its use of a single tuning parameter to simultaneously achieve high reaching accuracy and stable behavior. Applications in teleoperation, servoing, and learning are demonstrated. Videos and code are available at https://jparse-manip.github.io/.
Sources
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- OpenVLA: An Open-Source Vision-Language-Action Model
- What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
- Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics
- Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies