Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

Customizing robotic behaviors to be aligned with diverse human preferences is an
underexplored challenge in the field of embodied AI. In this paper we present Promptable …

Reinforcement learning from human feedback (RLHF) alleviates the problem of designing a
task-specific reward function in reinforcement learning by learning it from human preference …

Zero-shot human-AI coordination holds the promise of collaborating with humans without
human data. Prevailing methods try to train the ego agent with a population of partners via …

Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real
world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is …

Over the past few years, artificial intelligence (AI) has achieved great success in a variety of
applications, such as image classification and recommendation systems. This success has …

Preference-based reinforcement learning (PbRL) is emerging as a promising approach to
teaching robots through human comparative feedback, sidestepping the need for complex …

Off-policy reinforcement learning algorithms maintain a replay buffer to utilize samples
obtained from earlier policies. The sampling strategy that prioritizes certain data in a buffer to …

This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to
address the exploration capability deficiency in existing music-conditioned 3D dance …

In this paper, we present a novel curriculum reinforcement learning method that can
automatically generate a high-performance autopilot controller for a 6-degree-of-freedom (6 …