Collective Learning for Unified Robotic Manipulation via Predictive Dynamics Adaptation

Key points

• Collective learning aggregates multi-robot experience to discover shared task structure, yet naive aggregation can average incompatible action modes when different embodiments realize the same Cartesian command through different transition behavior.1
• To address this, we propose PACE, a two-stage collective learning framework that separates shared task-space intent from embodiment-dependent transition dynamics.1
• The second stage introduces a decision layer equipped with a predictive dynamics adapter.1
• By forcing the latent state to predict future transitions, this adapter acts as a latent regularizer that grounds the policy in the transition behavior of the executing robot while preserving real-time inference without online planning.1

Sources (1)

• [1]Collective Learning for Unified Robotic Manipulation via Predictive Dynamics Adaptation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 08:09 PM

  Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodiment-dependent transition dynamics.

  Collective learning aggregates multi-robot experience to discover shared task structure, yet naive aggregation can average incompatible action modes when different embodiments realize the same Cartesian command through different transition behavior.

Extractive summary: sentences quoted from the sources.