Towards Efficient Robotic Manipulation Models with Self-Recursive Pruning

Key points

• Network pruning can reduce parameter redundancy in robotic policies.1
• However, generic pruning criteria are tailored for image recognition tasks and commonly designed to preserve weight magnitude, local reconstruction, or language-model likelihood rather than closed-loop action behavior.1
• Directly applying these pruning algorithms to robotic tasks yields unsatisfactory performance.1
• We further design a self-recursive coarse-to-fine procedure: importance is recalibrated after each nested coarse pruning stage, while held-out offline action distortion guides fine-grained budget allocation after a sparsity knee.1

Sources (1)

• [1]Towards Efficient Robotic Manipulation Models with Self-Recursive Pruning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 03:40 PM

  In this paper, we propose Loss-Conditioned Activation-Moment (LCAM) pruning, a training-free method for unstructured pruning of pre-trained robotic manipulation policies.

  Network pruning can reduce parameter redundancy in robotic policies.

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