Reproduce “Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning” on OpenResearch

About this paper

Aditya Ganapathi, Pete Florence, Jake Varley, Kaylee Burns, Ken Goldberg, Andy Zeng

A new framework, Implicit Kinematic Policies (IKP), integrates joint and Cartesian action spaces within end-to-end robot learning via a differentiable kinematic module. It dynamically leverages the most appropriate action representation for a given task and incorporates learnable residuals to compensate for joint encoder miscalibration, yielding higher success rates in diverse robotic manipulation scenarios.

Key insights

• Implicit policy formulations naturally enable the simultaneous learning from multiple kinematically consistent action representations without incurring conflicting gradients or reliance on inverse kinematics.
• The deep network, through differentiable kinematics, can adaptively leverage the action space (joint or Cartesian) that presents the most learnable (e.g., lower-frequency) patterns for a given task.
• By introducing learnable residual blocks into the kinematic chain, policies can effectively compensate for systematic hardware imperfections like joint encoder miscalibration, enhancing real-world robustness.

Summary by alphaXiv.