DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration

Dynamic manipulation is a critical capability for robots operating in complex and dynamic environments, where robots must interact with objects that are moving or require rapid adjustments. However, learning models for dynamic manipulation tasks face two major challenges: (1) the combinatorial complexity of dynamic scenarios leads to substantial data requirements, and (2) rapid variations in dynamics require real-time and accurate policy execution. In this paper, we propose DynamicManip to address these challenges through an efficient data augmentation pipeline and a low-latency imitation policy. We first propose a static-to-dynamic augmentation pipeline that synthesizes diverse dynamic manipulation demonstrations from a single static demonstration. Second, we introduce a dynamic-aware adaptive policy that adjusts its inference frequency according to task dynamics, enabling responsive and effective dynamic manipulation. Third, we build a dynamic manipulation benchmark, which includes diverse dynamic tasks with an automatic evaluation system for scalable and consistent assessment. Extensive experiments in both simulation and the real world demonstrate that DynamicManip not only provides significant improvements in data efficiency but also achieves better performance in dynamic manipulation tasks, with a mean success rate 18.4 percentage points higher and policy-query latency 32.9% lower.

Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum

In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this task poses two key challenges: 1) Spatial understanding is challenging in complex 3D environments with diverse spatial relations. 2) Action generation is difficult to generalize, as limited and costly real-robot data restricts data-driven models generalization. To address these challenges, we propose a generalizable humanoid loco-manipulation framework that leverages the spatial perception and action generation capabilities of multi-agent large models. Specifically, our framework includes two components: Active Spatial Brain for active spatial perception and decision-making, and Generalizable Action Cerebellum for executable robot action generation. The first component actively perceives the spatial scene and makes decisions on task planning and subtask decomposition. The second component generate executable robot actions based on the decisions made by the first module without needs of task-specific real robot data. To benchmark our framework, we design a set of spatial manipulation tasks from two perspectives: evaluating spatial perception and understanding, and assessing real-robot task performance. The results demonstrate strong performance on both aspects across diverse tasks and environments.

OmniDexGrasp: Generalizable Dexterous Grasping via Foundation Model and Force Feedback

Enabling robots to dexterously grasp and manipulate objects based on human commands is a promising direction in robotics. However, existing approaches are challenging to generalize across diverse objects or tasks due to the limited scale of semantic dexterous grasp datasets. Foundation models offer a new way to enhance generalization, yet directly leveraging them to generate feasible robotic actions remains challenging due to the gap between abstract model knowledge and physical robot execution. To address these challenges, we propose OmniDexGrasp, a generalizable framework that achieves omni-capabilities in user prompting, dexterous embodiment, and grasping tasks by combining foundation models with the transfer and control strategies. OmniDexGrasp integrates three key modules: (i) foundation models are used to enhance generalization by generating human grasp images supporting omni-capability of user prompt and task; (ii) a human-image-to-robot-action transfer strategy converts human demonstrations into executable robot actions, enabling omni dexterous embodiment; (iii) force-aware adaptive grasp strategy ensures robust and stable grasp execution. Experiments in simulation and on real robots validate the effectiveness of OmniDexGrasp on diverse user prompts, grasp task and dexterous hands, and further results show its extensibility to dexterous manipulation tasks.