Task-oriented grasping (TOG), which refers to the problem of 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 complex relationship between objects, tasks, and grasps requires rich prior knowledge about objects and tasks. Existing methods typically limit the prior knowledge to a closed-set scope and cannot support the 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. Comprehensive experiments are conducted on the contributed Language and Vision Augmented TaskGrasp (LaViA-TaskGrasp) dataset, demonstrating the superiority of FoudationGrasp over existing methods when generalizing to novel object instances, object classes, and tasks out of the training set. Furthermore, the effectiveness of FoudationGrasp is validated in real-robot grasping and manipulation experiments on a 7 DoF robotic arm. Our code, data, appendix, and video are publicly available at https://sites.google.com/view/foundationgrasp.

Deep neural networks are susceptible to generating overconfident yet erroneous predictions when presented with data beyond known concepts. This challenge underscores the importance of detecting out-of-distribution (OOD) samples in the open world. In this work, we propose a novel feature-space OOD detection score based on class-specific and class-agnostic information. Specifically, the approach utilizes Whitened Linear Discriminant Analysis to project features into two subspaces - the discriminative and residual subspaces - for which the in-distribution (ID) classes are maximally separated and closely clustered, respectively. The OOD score is then determined by combining the deviation from the input data to the ID pattern in both subspaces. The efficacy of our method, named WDiscOOD, is verified on the large-scale ImageNet-1k benchmark, with six OOD datasets that cover a variety of distribution shifts. WDiscOOD demonstrates superior performance on deep classifiers with diverse backbone architectures, including CNN and vision transformer. Furthermore, we also show that WDiscOOD more effectively detects novel concepts in representation spaces trained with contrastive objectives, including supervised contrastive loss and multi-modality contrastive loss.

We propose a new 6-DoF grasp pose synthesis approach from 2D/2.5D input based on keypoints. Keypoint-based grasp detector from image input has demonstrated promising results in the previous study, where the additional visual information provided by color images compensates for the noisy depth perception. However, it relies heavily on accurately predicting the location of keypoints in the image space. In this paper, we devise a new grasp generation network that reduces the dependency on precise keypoint estimation. Given an RGB-D input, our network estimates both the grasp pose from keypoint detection as well as scale towards the camera. We further re-design the keypoint output space in order to mitigate the negative impact of keypoint prediction noise to Perspective-n-Point (PnP) algorithm. Experiments show that the proposed method outperforms the baseline by a large margin, validating the efficacy of our approach. Finally, despite trained on simple synthetic objects, our method demonstrate sim-to-real capacity by showing competitive results in real-world robot experiments.

Great success has been achieved in the 6-DoF grasp learning from the point cloud input, yet the computational cost due to the point set orderlessness remains a concern. Alternatively, we explore the grasp generation from the RGB-D input in this paper. The proposed solution, Keypoint-GraspNet, detects the projection of the gripper keypoints in the image space and then recover the SE(3) poses with a PnP algorithm. A synthetic dataset based on the primitive shape and the grasp family is constructed to examine our idea. Metric-based evaluation reveals that our method outperforms the baselines in terms of the grasp proposal accuracy, diversity, and the time cost. Finally, robot experiments show high success rate, demonstrating the potential of the idea in the real-world applications.

Home-assistant robots have been a long-standing research topic, and one of the biggest challenges is searching for required objects in housing environments. Previous object-goal navigation requires the robot to search for a target object category in an unexplored environment, which may not be suitable for home-assistant robots that typically have some level of semantic knowledge of the environment, such as the location of static furniture. In our approach, we leverage this knowledge and the fact that a target object may be located close to its related objects for efficient navigation. To achieve this, we train a graph neural network using the Visual Genome dataset to learn the object co-occurrence relationships and formulate the searching process as iteratively predicting the possible areas where the target object may be located. This approach is entirely zero-shot, meaning it doesn't require new accurate object correlation in the test environment. We empirically show that our method outperforms prior correlational object search algorithms. As our ultimate goal is to build fully autonomous assistant robots for everyday use, we further integrate the task planner for parsing natural language and generating task-completing plans with object navigation to execute human instructions. We demonstrate the effectiveness of our proposed pipeline in both the AI2-THOR simulator and a Stretch robot in a real-world environment.