9 accepted papers
Vision-based reinforcement learning can benefit from object-centric scene representation, which factorizes the visual observation into individual objects and their attributes, such as color, shape, size, and position. While such object-centric representations can extract components that generalize w…
In offline goal-conditioned reinforcement learning (GCRL), where one relies on a limited reward-free dataset to learn a generalist goal-reaching agent, compositional generalization becomes essential for reaching unseen goals under novel contextual variations. Most prior approaches pursue this via tr…
Skill discovery methods enable agents to tackle intricate tasks by acquiring diverse and useful skills from task-agnostic datasets in an unsupervised manner. To apply these methods to more general and everyday tasks, the skill set must be scalable. However, current approaches struggle with this scal…
In real-world applications, a reinforcement learning (RL) agent should consider multiple objectives and adhere to safety guidelines. To address these considerations, we propose a constrained multi-objective RL algorithm named constrained multi-objective gradient aggregator (CoMOGA). In the field of…
Skill discovery methods enable agents to tackle intricate tasks by acquiring diverse and useful skills from task-agnostic datasets in an unsupervised manner. To apply these methods to more general and everyday tasks, the skill set must be scalable. However, current approaches struggle with this scal
Addressing decision-making problems using sequence modeling to predict future trajectories shows promising results in recent years. In this paper, we take a step further to leverage the sequence predictive method in wider areas such as long-term planning, vision-based control, and multi-task decisio…
In this letter, we consider vision-based control tasks of which the desired goals are given as target images. The problems are often addressed by an autonomous agent which optimizes a trajectory to minimize a manually designed cost function. However, it is challenging to design a suitable cost funct
In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinforcement learning (RL). A Tsallis MDP provides a unified framework for the original RL problem and RL with various types…
Soft mobile robots have shown great potential in unstructured and confined environments by taking advantage of their excellent adaptability and high dexterity. However, there are several issues to be addressed, such as actuating speeds and controllability, in soft robots. In this letter, a new vibra