We address the challenge of effectively controlling the locomotion of legged robots by incorporating precise frequency and phase characteristics, which is often ignored in locomotion policies that do not account for the periodic nature of walking. We propose a hierarchical architecture that integrates a low-level phase tracker, oscillators, and a high-level phase modulator. This controller allows quadruped robots to walk in a natural manner that is synchronized with external musical rhythms. Our method generates diverse gaits across different frequencies and achieves real-time synchronization with music in the physical world. This research establishes a foundational framework for enabling real-time execution of accurate rhythmic motions in legged robots. The video and code are available at https://music-walker.github.io/.

Learning diverse skills for quadruped robots presents significant challenges, such as mastering complex transitions between different skills and handling tasks of varying difficulty. Existing imitation learning methods, while successful, rely on expensive datasets to reproduce expert behaviors. Inspired by introspective learning, we propose Progressive Adversarial Self-Imitation Skill Transition (PASIST), a novel method that eliminates the need for complete expert datasets. PASIST autonomously explores and selects high-quality trajectories based on predefined target poses instead of demonstrations, leveraging the Generative Adversarial Self-Imitation Learning (GASIL) framework. To further enhance learning, We develop a skill selection module to mitigate mode collapse by balancing the weights of skills with varying levels of difficulty. Through these methods, PASIST is able to reproduce skills corresponding to the target pose while achieving smooth and natural transitions between them. Evaluations on both simulation platforms and the Solo 8 robot confirm the effectiveness of PASIST, offering an efficient alternative to expert-driven learning.

Due to increasing complexity of traffic conditions, accurately predicting future trajectories of traffic agents is essential for driving efficiency and collision avoidance, making it vital to the decision-making of autonomous vehicles. However, it presents a significant challenge in trajectory prediction owing to the complex road topology and uncertainty of drivers' intention. In this paper, we introduce$\mathrm{A}^{3} \mathrm{E}^{2} \text{Net}$, a multi-modal trajectory prediction network that integrates waypoints, lanes, and motion traces, which collectively constitute the driving environment. It employs self-attention mechanism for waypoint representation, a Graph Neural Network (GNN) for road topology, and a spatiotemporal Transformer to encode motion dynamics. Subsequently, a cross-attention module fuses context information across granularities and captures agent-environment interactions. Finally, inspired by the outstanding performance of the autoregressive model in large language models (LLMs), we propose a goal-driven autoregression-based trajectory generation module to introduce both temporal logic and latent intentions, and thus enable precise and diverse multi-modal trajectory prediction (MTP). Evaluations on the Argoverse motion forecasting benchmark highlight our method's state-of-the-art performance and computational efficiency.

Dance has been an important art form and means of communication since the dawn of human civilization. Equipping humanoid robots with the ability to perform smooth dance movements to music is a key research priority in artificial intelligence, robotics and human-computer interaction. However, existing kinematics-based dance generation methods often violate real-world physical laws as they do not consider physical constraints, leading to unrealistic movements. Additionally, due to the diversity and dynamic variability of input music, most existing physics-based methods, which rely on task-specific reward functions, face significant challenges in effectively handling music-driven dance generation tasks. To address these issues, we introduce MDRPC, the first physics-based, music-driven dance generation method for humanoid robots. Inspired by human choreographic principles, MDRPC is defined as a two-phase framework. The initial phase utilizes adversarial imitation learning to acquire a rich set of reusable dance primitives from a music-dance dataset. In the subsequent phase, these dance primitives are orchestrated under the guidance of musical theory and choreographic rules to generate complex humanoid dance sequences. Specifically, we propose beat alignment and dance diversity reward functions to synchronize motion rhythms with music beats and enhance the diversity of dance movements. We implement MDRPC on a simulated humanoid robot, and the results confirm that our method effectively controls the humanoid, enabling it to perform dance movements harmoniously synchronized with the music.