SeqWM introduces sequential autoregressive agent-wise world models for multi-robot MBRL, outperforming baselines in performance and sample efficiency on Bi-DexHands and Multi-Quadruped tasks with physical robot deployment.
Simplicial embeddings in self-supervised learning and downstream classification
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
TD-MPC2 scales an implicit world-model RL method to a 317M-parameter agent that masters 80 tasks across four domains with a single hyperparameter configuration.
citing papers explorer
-
Empowering Multi-Robot Cooperation via Sequential World Models
SeqWM introduces sequential autoregressive agent-wise world models for multi-robot MBRL, outperforming baselines in performance and sample efficiency on Bi-DexHands and Multi-Quadruped tasks with physical robot deployment.
-
TD-MPC2: Scalable, Robust World Models for Continuous Control
TD-MPC2 scales an implicit world-model RL method to a 317M-parameter agent that masters 80 tasks across four domains with a single hyperparameter configuration.