Fields

Pick a domain and keep going deeper: field, subfield, topic. Every step lists the papers at that point or below it. The counts are papers that are accepted or in review; papers that were not accepted are in the full library.

Reinforcement learning 0

Model-free deep RL (discrete and continuous control) 0
Model-based RL and world models 0
Offline RL and offline-to-online 0
Exploration and intrinsic motivation 0
Meta-RL, multi-task RL and generalisation 0

Multi-agent learning and systems 0

Cooperative multi-agent reinforcement learning 0
Emergent communication and learned communication protocols 0
LLM-based multi-agent systems 0
Game-theoretic learning and zero-shot coordination 0

Robotics and embodied AI 0

Robot manipulation and imitation learning (including vision-language-action models) 0
Legged and humanoid locomotion 0
Navigation, state estimation and SLAM 0
Motion planning and control (MPC, trajectory optimisation) 0
Multi-robot systems and communication 0

Control systems and learning-based control 0

Optimal control and model predictive control 0
Learning-based control and system identification 0
Safe control (control barrier functions, reachability, safe learning) 0
Physics-based cost functions and differentiable simulation 0

Domain

Robotics, control and decision making

Agents that act, alone or in groups, from learned policies to controllers with guarantees and physical robots.

Fields

Robotics, control and decision making

No papers here yet