Humanoid robots require both robust lower-body locomotion and precise upper-body manipulation. While recent Reinforcement Learning (RL) approaches provide whole-body loco-manipulation policies, they lack precise manipulation with high DoF arms. In this paper, we propose decoupling upper-body control from locomotion, using inverse kinematics (IK) and motion retargeting for precise manipulation, while RL focuses on robust lower-body locomotion. We introduce PMP (Predictive Motion Priors), trained with Conditional Variational Autoencoder (CVAE) to effectively represent upper-body motions. The locomotion policy is trained conditioned on this upper-body motion representation, ensuring that the system re-mains robust with both manipulation and locomotion. We show that CVAE features are crucial for stability and robustness, and significantly outperforms RL-based whole-body control in precise manipulation. With precise upper-body motion and robust lower-body locomotion control, operators can remotely control the humanoid to walk around and explore different environments, while performing diverse manipulation tasks.

‘In-the-wild’ mobile manipulation aims to deploy robots in diverse real-world environments, which requires the robot to (1) have skills that generalize across object configurations; (2) be capable of long-horizon task execution in diverse environments; and (3) perform complex manipulation beyond pick-and-place. Quadruped robots with manipulators hold promise for extending the workspace and enabling robust locomotion, but existing results do not investigate such a capability. This paper proposes WildLMa with three components to address these issues: (1) adaptation of learned low-level controller for VR-enabled whole-body teleoperation and traversability; (2) WildLMa-Skill - a library of generalizable visuomotor skills acquired via imitation learning or heuristics and (3) WildLMa-Planner - an interface of learned skills that allow LLM planners to coordinate skills for long-horizon tasks. We demonstrate the importance of high-quality training data by achieving higher grasping success rate over existing RL baselines using only tens of demonstrations. WildLMa exploits CLIP for language-conditioned imitation learning that empirically generalizes to objects unseen in training demonstrations. Besides extensive quantitative evaluation, we qualitatively demonstrate practical robot applications, such as cleaning up trash in university hallways or outdoor terrains, operating articulated objects, and rearranging items on a bookshelf.

In the deep learning era, 2D LiDAR perception is often overlooked as research prioritizes 3D point clouds. Yet, 2D LiDAR remains essential for low-cost robotic systems due to its affordability. Despite its simplicity, it faces major challenges-not only in perception but also in learning itself, as data sparsity and limited features hinder effective framework development. Additionally, fundamental structural differences prevent direct adaptation of 3D perception networks. To address these aforementioned concerns, we proposes LSW-Net (Laser Scan Weight-Net), a self-supervised framework for 2D LiDAR perception, enabling end-to-end learning from raw point clouds to adaptive weight extraction. This framework provides a scalable and lightweight solution for 2D LiDAR environmental perception, and its self-supervised nature reduces annotation costs.It includes: (i) a general 2D Laser Encoder (LS-Encoder) that integrates local convolutional perception with global attention perception to extract multi-scale spatio-temporal features; and (ii) an interpretable weight extraction module (Weight Extractor) that dynamically quantifies the contribution of each point in environmental perception tasks through contrastive learning and physical consistency constraints. Evaluated on diverse scenes for point cloud registration and SLAM tasks, LSW-Net outperforms traditional methods in feature discriminability and adaptability. Additionally, we performed ablation experiments to substantiate the rationality of our approach. Our code is available at https://github.com/cuiyujie0113/LSW-NET.