RL-IK

Table of contents

1. Core ideas
2. Typical workflow
3. Manipulation relevance

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RL-IK uses reinforcement learning to solve inverse kinematics, learning a policy that maps desired end-effector poses to joint configurations.

Core ideas

• Treat IK as a sequential decision problem
• Learn collision-aware or redundancy-aware solutions from interaction
• Complement classical analytic and numerical IK solvers

Typical workflow

1. Define the target end-effector pose and robot state.
2. Roll out an RL policy that proposes joint updates or configurations.
3. Evaluate tracking error, limits, and collision constraints.
4. Deploy the learned IK policy for reaching and manipulation.

Manipulation relevance

• Useful when classical IK is underdetermined or fails near singularities
• Can incorporate task and collision preferences into reaching
• Connects kinematics with learned control for downstream manipulation