Under-Review

This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling (IDS).

we propose a novel Vision-Language Preference learning framework that learns a vision-language preference model to provide preference feedback for embodied manipulation tasks.

We decompose the reward value in RLHF into two independent components that consists prompt-free reward and prompt-related reward, and propose a new reward learning algorithm by prioritizing data samples based on their prompt-free reward values.

We propose Adversarial Locomotion and Motion Imitation (ALMI) for humanoid robots, which serves as a novel framework for loco-manipulation tasks, enabling adversarial policy learning between upper and lower body.

We propose Diffusion-Inspired Multi-Agent world model (DIMA), a novel framework for multi-agent reinforcement learning that leverages diffusion models to reduce modeling complexity and improve sample efficiency.

We propose the Learn as Individuals, Evolve as a Team (LIET) framework to enable multi-agent LLMs to adapt to embodied environments through individual learning and team evolution

We propose a novel bimanual foundation policy that leverages text-to-video models to predict robot trajectories and uses optical flow as an intermediate variable to improve generalization.

We propose a novel framework that enables humanoid robots to traverse complex terrains with controllable human-like gaits using a mixture of latent residual experts and multi-discriminators.

We propose a physics-based humanoid control framework, aiming to master highly-dynamic human behaviors such as Kungfu and dancing through multi-steps motion processing and adaptive motion tracking.