Slot-MPC learns slot representations to build a differentiable object-centric dynamics model that supports efficient gradient-based MPC for robotic manipulation in novel situations.
arXiv preprint arXiv:2512.09929 , year=
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Planning with a frozen egocentric world model is lifted from 48-dim joint actions to a few 2D goal waypoints via a trained policy, reducing CEM error reduction by 3.8x.
AdaJEPA performs closed-loop test-time adaptation of latent world models during MPC by executing an action chunk, observing the transition, and taking one gradient step on the model before replanning, yielding higher goal-reaching success.
WestWorld introduces a scalable trajectory world model with Sys-MoE routing via system embeddings and structural embeddings for physical knowledge, pretrained on 89 environments to improve zero-shot prediction and real-robot control.
citing papers explorer
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Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations
Slot-MPC learns slot representations to build a differentiable object-centric dynamics model that supports efficient gradient-based MPC for robotic manipulation in novel situations.
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Lifting Embodied World Models for Planning and Control
Planning with a frozen egocentric world model is lifted from 48-dim joint actions to a few 2D goal waypoints via a trained policy, reducing CEM error reduction by 3.8x.
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AdaJEPA: An Adaptive Latent World Model
AdaJEPA performs closed-loop test-time adaptation of latent world models during MPC by executing an action chunk, observing the transition, and taking one gradient step on the model before replanning, yielding higher goal-reaching success.
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WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
WestWorld introduces a scalable trajectory world model with Sys-MoE routing via system embeddings and structural embeddings for physical knowledge, pretrained on 89 environments to improve zero-shot prediction and real-robot control.