MuJoCo-Based Robotic Manipulator Simulation (UR10e)
Recruiter summary
- Built: Four UR10e simulation configurations spanning forward and inverse kinematics with position and velocity command interfaces.
- Validated: Controller behavior was compared through convergence, trajectory smoothness, and Cartesian end-effector visualization in four recorded demonstrations.
- Why it matters: Makes command-interface and kinematic-control tradeoffs visible in a controlled physics environment before hardware deployment.
Overview
Developed a physics-based simulation of a 6-DOF UR10e manipulator in MuJoCo to compare joint-space vs task-space control and study how position vs velocity command interfaces affect stability, convergence, and trajectory smoothness.
Key contributions
- Implemented FK and IK control pipelines under both position and velocity command modes
- Evaluated controller behavior via convergence and qualitative trajectory smoothness comparisons
- Visualized end-effector motion in Cartesian space to validate and debug controller behavior
Simulation Environment
- Robot model: UR10e (6-DOF serial manipulator)
- Simulator: MuJoCo
- Implementation: Python control loop with real-time visualization
Control configurations evaluated
- FK Position
- FK Velocity
- IK Position
- IK Velocity
Control Implementation
Forward Kinematics (FK)
Joint-space targets are issued directly and propagated through the kinematic chain. FK modes serve as a stable baseline for validating joint-level motion and convergence.
Inverse Kinematics (IK)
Cartesian targets (or Cartesian velocities) are mapped to joint commands using inverse kinematics / Jacobian relationships. IK enables intuitive task-space behavior but introduces solver sensitivity and constraint considerations.
Position vs Velocity Interfaces
- Position commands: faster convergence but more abrupt transitions
- Velocity commands: smoother trajectories and more continuous motion behavior
Control Experiments and Results
FK — Position Control
Direct joint position commands used to validate joint-level convergence and establish baseline behavior.
FK — Velocity Control
Joint velocity commands produced smoother transitions and reduced motion discontinuities compared to position control.
IK — Position Control
Task-space targets mapped to joint configurations using inverse kinematics. Enabled intuitive Cartesian targeting while requiring solver convergence and constraint awareness.
IK — Velocity Control
End-effector velocity commands mapped to joint velocities using Jacobian relationships, producing smooth and continuous Cartesian trajectories.
Engineering Insights
- FK control is stable and predictable, but less intuitive for task-space objectives
- IK control enables direct Cartesian motion but is more sensitive to numerical effects and constraints
- Velocity interfaces yielded smoother motion and reduced discontinuities vs position commands
- Physics-based simulation helped surface motion artifacts not visible in purely kinematic testing
- Trajectory visualization significantly accelerated debugging and qualitative validation
Technical Stack
- MuJoCo
- Python
- Forward and Inverse Kinematics
- Jacobian-based velocity control
- End-effector trajectory visualization
Future Improvements
- Add torque-based control via MuJoCo inverse dynamics and compare against position/velocity interfaces
- Quantitatively benchmark tracking error and convergence across trajectories
- Extend to contact-rich manipulation tasks (object interaction)
- Evaluate controller behavior under dynamic loading and constraint variation
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