arXiv Machine Learning
Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
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arXiv AI
Sep 30
Beyond a single latent space: a dual-latent world model for long-horizon planning
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
arXiv Machine Learning
Jul 9
Latent Policy Steering through One-Step Flow Policies
arXiv:2603. 05296v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration.
arXiv AI
Jun 3
ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL
arXiv:2606. 03017v1 Announce Type: cross Abstract: Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals.
arXiv AI
Oct 21
Exploring Temporal Context-Aware Physical Grounding in Latent World Models
arXiv:2610.28483v1 Announce Type: new Abstract: Learning latent dynamics is fundamental to building action-conditioned world models for planning. Among existing families, Joint Embedding Predictive A...
arXiv Machine Learning
Oct 7
MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning
arXiv:2605.09364v2 Announce Type: replace Abstract: This paper investigates robust representation learning in offline goal-conditioned reinforcement learning (GCRL). Particularly in sparse reward sce...
arXiv AI
Jun 16
LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure
arXiv:2606. 15306v1 Announce Type: cross Abstract: We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared across those tasks and use it to improve future decisions.