Collective Learning for Unified Robotic Manipulation via Predictive Dynamics Adaptation
Overview
PACE, a two-stage framework, separates task intent from dynamics. Sim-to-real transfer validated with KUKA robot, achieving 93% success in simulation. Research published on arXiv Robotics.
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Environment: simulation
“level context from collective data. The second stage introduces a decision layer equipped with a predictive dynamics adapter. 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. Evaluations across simulated embodiments demonstrate that”
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- arXiv Robotics — research abstractsCollective Learning for Unified Robotic Manipulation via Predictive Dynamics Adaptation
This research proposes PACE, a two-stage collective learning framework that separates shared task-space intent from embodiment-dependent transition dynamics. The first stage uses a pre-trained representation layer to extract task-level context, while the second stage introduces a predictive dynamics adapter that grounds policies in the executing robot's transition behavior. Evaluations show PACE achieves over 93% success in simulation and validates sim-to-real transfer with a physical KUKA robot.
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