Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning, details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain.

Task-oriented grasping (TOG) is crucial for robots to accomplish manipulation tasks, requiring the determination of TOG positions and directions. Existing methods either rely on costly manual TOG annotations or only extract coarse grasping positions or regions from human demonstrations, limiting their practicality in real-world applications. To address these limitations, we introduce RTAGrasp, a Retrieval, Transfer, and Alignment framework inspired by human grasping strategies. Specifically, our approach first effortlessly constructs a robot memory from human grasping demonstration videos, extracting both TOG position and direction constraints. Then, given a task instruction and a visual observation of the target object, RTAGrasp retrieves the most similar human grasping experience from its memory and leverages semantic matching capabilities of vision foundation models to transfer the TOG constraints to the target object in a training-free manner. Finally, RTAGrasp aligns the transferred TOG constraints with the robot's action for execution. Evaluations on the public TOG benchmark, TaskGrasp dataset, show the competitive performance of RTAGrasp on both seen and unseen object categories compared to existing baseline methods. Real-world experiments further validate its effectiveness on a robotic arm. Our code, appendix, and video are available at https://sites.google.com/view/rtagrasp/home.

Task-oriented grasping (TOG) is essential for robots to perform manipulation tasks, requiring grasps that are both stable and compliant with task-specific constraints. Humans naturally grasp objects in a task-oriented manner to facilitate subsequent manipulation tasks. By leveraging human grasp demonstrations, current methods can generate high-quality robotic parallel-jaw task-oriented grasps for diverse objects and tasks. However, they still encounter challenges in maintaining grasp stability and sampling efficiency. These methods typically rely on a two-stage process: first performing exhaustive task-agnostic grasp sampling in the 6-DoF space, then applying demonstration-induced constraints (e.g., contact regions and wrist orientations) to filter candidates. This leads to inefficiency and potential failure due to the vast sampling space. To address this, we propose the Human-guided Grasp Diffuser (HGDiffuser), a diffusion-based framework that integrates these constraints into a guided sampling process. Through this approach, HGDiffuser directly generates 6-DoF task-oriented grasps in a single stage, eliminating exhaustive task-agnostic sampling. Furthermore, by incorporating Diffusion Transformer (DiT) blocks as the feature backbone, HGDiffuser improves grasp generation quality compared to MLP-based methods. Experimental results demonstrate that our approach significantly improves the efficiency of task-oriented grasp generation, enabling more effective transfer of human grasping strategies to robotic systems. To access the source code and supplementary videos, visit https://sites.google.com/ view/hgdiffuser.

Robotic instruction following tasks require seamless integration of visual perception, task planning, target localization, and motion execution. However, existing task planning methods for instruction following are either data-driven or underperform in zero-shot scenarios due to difficulties in grounding lengthy instructions into actionable plans under operational constraints. To address this, we propose FlowPlan, a structured multi-stage LLM workflow that elevates zero-shot pipeline and bridges the performance gap between zero-shot and data-driven in-context learning methods. By decomposing the planning process into modular stages—task information retrieval, language-level reasoning, symbolic-level planning, and logical evaluation—FlowPlan generates logically coherent action sequences while adhering to operational constraints and further extracts contextual guidance for precise instance-level target localization. Benchmarked on ALFRED and validated in real-world applications, our method achieves competitive performance relative to data-driven in-context learning methods and demonstrates adaptability across diverse environments. This work advances zero-shot task planning in robotic systems without reliance on labeled data. Project website: https://instruction-following-project.github.io/.

Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone towards tool manipulation. Analogous to the activation of two brain regions responsible for semantic and geometric reasoning during cognitive processes, modeling the intricate relationship between objects, tasks, and grasps necessitates rich semantic and geometric prior knowledge about these elements. Existing methods typically restrict the prior knowledge to a closed-set scope, limiting their generalization to novel objects and tasks out of the training set. To address such a limitation, we propose FoundationGrasp, a foundation model-based TOG framework that leverages the open-ended knowledge from foundation models to learn generalizable TOG skills. Extensive experiments are conducted on the contributed Language and Vision Augmented TaskGrasp (LaViA-TaskGrasp) dataset, demonstrating the superiority of FoundationGrasp over existing methods when generalizing to novel object instances, object classes, and tasks out of the training set. Furthermore, the effectiveness of FoundationGrasp is validated in real-robot grasping and manipulation experiments on a 7-DoF robotic arm. Our code, data, appendix, and video are publicly available athttps://sites.google.com/view/foundationgrasp. Note to Practitioners—This research is motivated by the challenge of generalizable task-oriented grasping skill learning. Solving such a challenge could significantly improve the robot’s level of automation and intelligence in tool manipulation for household and industrial tasks. Existing methods struggle with handling unseen objects and tasks in dynamic, open-world environments. To overcome this limitation, we propose to leverage the open-ended knowledge from foundation models to improve the generalization capabilities of existing TOG methods. This way, the robot can perform TOG w.r.t. unseen objects and tasks, facilitating downstream tool manipulation. Overall, this research has broad applicability to various scenarios involving tool manipulation, such as cleaning kitchenware and assembling parts in industrial contexts.

The prospect of assistive robots aiding in object organization has always been compelling. In an image-goal setting, the robot rearranges the current scene to match the single image captured from the goal scene. The key to an image-goal rearrangement system is estimating the desired placement pose of each object based on the single goal image and observations from the current scene. In order to establish sufficient associations for accurate estimation, the system should observe an object from a viewpoint similar to that in the goal image. Existing image-goal rearrangement systems, due to their reliance on a fixed viewpoint for perception, often require redundant manipulations to randomly adjust an object’s pose for a better perspective. Addressing this inefficiency, we introduce a novel object rearrangement system that employs multi-view fusion. By observing the current scene from multiple viewpoints before manipulating objects, our approach can estimate a more accurate pose without redundant manipulation times. A standard visual localization pipeline at the object level is developed to capitalize on the advantages of multi-view observations. Simulation results demonstrate that the efficiency of our system outperforms existing single-view systems. The effectiveness of our system is further validated in a physical experiment. For videos, please visit https: //sites.google.com/view/multi-view-rearr.

Object search is a fundamental skill for household robots, yet the core problem lies in the robot’s ability to locate the target object accurately. The dynamic nature of household environments, characterized by the arbitrary placement of daily objects by users, makes it challenging to perform target localization. To efficiently locate the target object, the robot needs to be equipped with knowledge at both the object and room level. However, existing approaches rely solely on one type of knowledge, leading to unsatisfactory object localization performance and, consequently, inefficient object search processes. To address this problem, we propose a commonsense scene graph-based target localization, CSG-TL, to enhance target object search in the household environment. Given the pre-built map with stationary items, the robot models the room-level spatial knowledge with object-level commonsense knowledge generated by a large language model (LLM) to a commonsense scene graph (CSG), supporting both types of knowledge for CSG-TL. To demonstrate the superiority of CSG-TL on target localization, extensive experiments are performed on the real-world ScanNet dataset and the AI2THOR simulator. Moreover, we have extended CSG-TL to an object search framework, CSG-OS, validated in both simulated and real-world environments. Code and videos are available at https://sites.google.com/view/csg-os.

To perform household tasks, assistive robots receive commands in the form of user language instructions for tool manipulation. The initial stage involves selecting the intended tool (i.e., object grounding) and grasping it in a task-oriented manner (i.e., task grounding). Nevertheless, prior researches on visual-language grasping (VLG) focus on object grounding, while disregarding the fine-grained impact of tasks on object grasping. Task-incompatible grasping of a tool will inevitably limit the success of subsequent manipulation steps. Motivated by this problem, this paper proposes GraspCLIP, which addresses the challenge of task grounding in addition to object grounding to enable task-oriented grasp prediction with visual-language inputs. Evaluation on a custom dataset demonstrates that GraspCLIP achieves superior performance over established baselines with object grounding only. The effectiveness of the proposed method is further validated on an assistive robotic arm for grasping previously unseen kitchen tools given the task specification. Our presentation video is available at: https://www.youtube.com/watch?v=e1wfYQPeAXU.

As the basics of Visual Simultaneous Localization And Mapping (VSLAM), keyframes play an essential role. In previous works, keyframes are selected according to a series of view change-based strategies for short-term data association (STDA). However, the texture enrichment of frames is always ignored, resulting in the failure of long-term data association (LTDA). In this paper, we propose an information enrichment selection strategy with an information occupancy grid model and a deep descriptor. Frame is expressed by a deep global descriptor for a statistical explainable abstraction, in which the texture enrichment is indicated. Based on the abstraction, an information occupancy grid model is established to measure the information enrichment and the potential LTDA ability. Evaluations on variant datasets are conducted, showing the advantage of our proposed method in terms of keyframe selection and tracking precision. Also, the statistical explainability of the deep descriptor is provided. The proposed keyframe selection strategy can improve LTDA and tracking precision, especially in situations with repeated observations and loop-closures.

Assistive robot systems have been developed to help people accomplish daily manipulation tasks especially for those with disabilities, where scene understanding plays a crucial role in enabling robots to interpret the surroundings and behave accordingly. Most of the current systems approach scene understanding without considering the functional dependencies between objects. However, it is only valuable to interact with some objects when their function-relevant counterparts are considered. In this paper, we augment an assistive robotic arm system with an end-to-end semantic relationship reasoning model. It incorporates functional relationships between pairs of objects for semantic scene understanding. To ensure good generalization to unseen objects and relationships, the model works in a category-agnostic manner. We evaluate our design and three baseline methods on a self-collected benchmark with two levels of difficulty. To further demonstrate the effectiveness, the model is integrated with a symbolic planner for goal-oriented, multi-step manipulation task on a real-world assistive robotic arm platform.

A segmentation-based architecture is proposed to decompose objects into multiple primitive shapes from monocular depth input for robotic manipulation. The backbone deep network is trained on synthetic data with 6 classes of primitive shapes generated by a simulation engine. Each primitive shape is designed with parametrized grasp families, permitting the pipeline to identify multiple grasp candidates per shape primitive region. The grasps are priority ordered via proposed ranking algorithm, with the first feasible one chosen for execution. On task-free grasping of individual objects, the method achieves a 94% success rate. On task-oriented grasping, it achieves a 76% success rate. Overall, the method supports the hypothesis that shape primitives can support task-free and task-relevant grasp prediction.