9 accepted papers
Goal-conditioned hierarchical reinforcement learning has demonstrated effectiveness in addressing complicated decision-making tasks by providing
Reinforcement learning (RL) typically presupposes instantaneous agent-environment interactions, but in real-world scenarios such as robotic control, overlooking observation delays can significantly impair performance. While existing studies consider stationary, known delays, real-world applications
Offline multi-agent reinforcement learning (MARL) with pre-training paradigm, which uses a large quantity of trajectories for offline pre-training and online deployment, has become fashionable lately. While performing well on various tasks, conventional pre-trained decision-making models based on im…
Causal dynamics models (CDMs) have demonstrated significant potential in addressing various challenges in reinforcement learning. To learn CDMs, recent studies have performed causal discovery to capture the causal dependencies among environmental variables. However, the learning of CDMs is still con…
Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. In this paper, we develop a novel framework for explainable RL by learning a causal world model without prior knowledge of the causal structure of the environment. The model…
Acquiring dynamics is critical for robot learning and is fundamental to planning and control. This paper concerns two fundamental questions: How can we learn a model that covers massive, diverse robot dynamics? Can we construct a model that lifts the data-collection pain and domain expertise require…
Intense spatiotemporal coupling states frequently appear in robotic tasks, and this coupling enriches the information encapsulated in each state. Taking advantage of historical observations can provide more information about the robot, especially for partially observable Markov decision processes. H
Kinematics and instantaneous kinematics are fundamental in many robotic tasks, such as positioning and collision avoidance. Existing learning methods mainly concern a single robot, and small-scale networks are sufficient for considerable approximation accuracy. A question is: Can we learn a kinemati…
This paper proposes two methods for collision detection between cylindrical components when mutual blocking occurs in the view of cameras. In reconstruction approach, 3D models are built according to key features captured by cameras and constraint optimization is employed to rapidly find possible in…