Goal-Conditioned RL

RL: Reinforcement Learning

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13 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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PlatoLTL: Scaling LTL-Guided Multi-Task RL
Jan 30, 2026Jacques Cloete, Mathias Jackermeier, Ioannis Havoutis +1RL for Robotics Constrained RL
Oxford Robotics Institute University of Oxford Oxford, United Kingdom · Department of Computer Science University of Oxford Oxford, United Kingdom
Consistent Zero-Shot Imitation with Contrastive Goal Inference
Oct 20, 2025Kathryn Wantlin, Chongyi Zheng, Benjamin EysenbachContrastive Learning Reinforcement Learning
Department of Computer Science, Princeton University, Princeton, NJ, USA.
Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration
Sep 12, 2025Chirayu Nimonkar, Shlok Shah, Catherine Ji +1Multi-Agent Coordination RL Exploration
Princeton University
Reward function compression facilitates goal-dependent reinforcement learning
Sep 8, 2025Gaia Molinaro, Anne G. E. CollinsReinforcement Learning Goal-Conditioned RL
University of California, Berkeley Berkeley, CA 94704
LHM-Humanoid: Long-Horizon Human Motion Control for Continuous Object Transport in Cluttered Scenes
Aug 23, 2025Haozhuo Zhang, Jingkai Sun, Michele Caprio +5Human Motion Generation Goal-Conditioned RL
The University of Manchester, Manchester, UK · X-Humanoid, Beijing, China · University of Hong Kong, Hong Kong, China +1
Hybrid Sequence Modeling and Reinforced Verification for Controllable Target-Conditioned Decision Making
Aug 22, 2025Yue Pei, Hongming Zhang, Chao Gao +7Reinforcement Learning Offline RL
School of Artificial Intelligence, Beihang University, Beijing, China · Department of Computing Science and Amii, University of Alberta, Edmonton, Canada · Edmonton Research Center, Huawei Canada, Edmonton, Canada +2
CONTHER: Context-Aware Reinforcement Learning for Robotic Manipulation with Sparse Rewards
Mar 20, 2025Maria Makarova, Qian Liu, Dzmitry TsetserukouReinforcement Learning Experience Replay
Intelligent Space Robotics Laboratory, Center for Digital Engineering, Skolkovo Institute of Science and Technology (Skoltech), 121205 Moscow, Russia · Department of Computer Science and Technology, Dalian University of Technology, China
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
Mar 19, 2025Kevin Wang, Ishaan Javali, Michał Bortkiewicz +2Reinforcement Learning Robotic RL
Princeton University · Warsaw University of Technology · Warsaw University of Technology, Tooploox, IDEAS Research Institute
ETHER: Aligning Emergent Communication for Hindsight Experience Replay
Jul 28, 2023Kevin Yandoka Denamganaï, Daniel Hernandez, Ozan Vardal +2Reinforcement Learning Experience Replay
Department of Computer Science University of York York, UK
UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors
Date pendingAtahan Cilan, Atay ÖzgövdeReinforcement Learning Human Behavior Simulation
Turkish Aerospace, Istanbul, Turkey · Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey

Jan 30, 2026, cs.LGJ/K move · Enter open · S save

PlatoLTL: Scaling LTL-Guided Multi-Task RL

Jacques Cloete, Mathias Jackermeier, Ioannis Havoutis, Alessandro Abate

Oxford Robotics Institute University of Oxford Oxford, United Kingdom · Department of Computer Science University of Oxford Oxford, United Kingdom

RL for Robotics Constrained RL Multi-Task RL Generalization in RL Goal-Conditioned RL

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Linear temporal logic (LTL) has emerged as a powerful formalism for specifying structured, temporally extended tasks in multi-task reinforcement learning (RL). However, while existing approaches in LTL-guided multi-task RL demonstrate success in simple environments, they suffer from challenges in representation learning and exploration efficiency when applied to high-dimensional environments with parameterized specifications. We present PlatoLTL, which elevates state-of-the-art methods to address both challenges. We model atomic propositions as instances of atomic predicates and inject task parameters directly into the goal embedding to enable efficient generalization. We also leverage simple priors on closeness to predicate satisfaction to enable and accelerate learning of complex tasks without biasing the optimal policy. We validate our approach on challenging environments including robotic manipulation and multi-drone navigation.

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