MoE-ACT: Scaling Multi-Task Bimanual Manipulation with Sparse Language-Conditioned Mixture-of-Experts Transformers

The ability of robots to handle multiple tasks under a unified policy is critical for deploying embodied intelligence in real-world household and industrial applications. However, out-of-distribution variation across tasks often causes severe task interference and negative transfer when training general robotic policies. To address this challenge, we propose a lightweight multi-task imitation learning framework for bimanual manipulation, termed Mixture-of-Experts-Enhanced Action Chunking Transformer (MoE-ACT), which integrates sparse Mixture-of-Experts (MoE) modules into the Transformer encoder of ACT. The MoE layer decomposes a unified task policy into independently invoked expert components. Through adaptive activation, it naturally decouples multi-task action distributions in latent space. During decoding, Feature-wise Linear Modulation (FiLM) dynamically modulates action tokens to improve consistency between action generation and task instructions. In parallel, multi-scale cross-attention enables the policy to simultaneously focus on both low-level and high-level semantic features, providing rich visual information for robotic manipulation. We further incorporate textual information, transitioning the framework from a purely vision-based model to a vision-centric, language-conditioned action generation system. Experimental validation in both simulation and a real-world dual-arm setup shows that MoE-ACT substantially improves multi-task performance. Specifically, MoE-ACT outperforms vanilla ACT by an average of 33% in success rate. These results indicate that MoE-ACT provides stronger robustness and generalization in complex multi-task bimanual manipulation environments. Our open-source project page can be found at https://j3k7.github.io/MoE-ACT/.

SpecFuse: A Spectral-Temporal Fusion Predictive Control Framework for UAV Landing on Oscillating Marine Platforms

Autonomous landing of Uncrewed Aerial Vehicles (UAVs) on oscillating marine platforms is severely constrained by wave-induced multi-frequency oscillations, wind disturbances, and prediction phase lags in motion prediction. Existing methods either treat platform motion as a general random process or lack explicit modeling of wave spectral characteristics, leading to suboptimal performance under dynamic sea conditions. To address these limitations, we propose SpecFuse: a novel spectral-temporal fusion predictive control framework that integrates frequency-domain wave decomposition with time-domain recursive state estimation for high-precision 6-DoF motion forecasting of Uncrewed Surface Vehicles (USVs). The framework explicitly models dominant wave harmonics to mitigate phase lags, refining predictions in real time via IMU data without relying on complex calibration. Additionally, we design a hierarchical control architecture featuring a sampling-based HPO-RRT* algorithm for dynamic trajectory planning under non-convex constraints and a learning-augmented predictive controller that fuses data-driven disturbance compensation with optimization-based execution. Extensive validations (2,000 simulations + 8 lake experiments) show our approach achieves a 3.2 cm prediction error, 4.46 cm landing deviation, 98.7% / 87.5% success rates (simulation / real-world), and 82 ms latency on embedded hardware, outperforming state-of-the-art methods by 44%-48% in accuracy. Its robustness to wave-wind coupling disturbances supports critical maritime missions such as search and rescue and environmental monitoring. All code, experimental configurations, and datasets will be released as open-source to facilitate reproducibility.