Jishnu Jaykumar P

Jishnu Jaykumar P, Yu-Wei Chao, Yu Xiang

We introduce the Few-Shot Object Learning (FewSOL) dataset for object recognition with a few images per object. We captured 336 real-world objects with 9 RGB-D images per object from different views. Object segmentation masks, object poses and object attributes are provided. In addition, synthetic images generated using 330 3D object models are used to augment the dataset. We investigated (i) few-shot object classification and (ii) joint object segmentation and few-shot classification with the state-of-the-art methods for few-shot learning and meta-learning using our dataset. The evaluation results show that there is still a large margin to be improved for few-shot object classification in robotic environments. Our dataset can be used to study a set of few-shot object recognition problems such as classification, detection and segmentation, shape reconstruction, pose estimation, keypoint correspondences and attribute recognition. The dataset and code are available at https://irvlutd.github.io/FewSOL.

Jishnu Jaykumar P, Kamalesh Palanisamy, Yu-Wei Chao et al.

We propose a novel framework for few-shot learning by leveraging large-scale vision-language models such as CLIP. Motivated by unimodal prototypical networks for few-shot learning, we introduce Proto-CLIP which utilizes image prototypes and text prototypes for few-shot learning. Specifically, Proto-CLIP adapts the image and text encoder embeddings from CLIP in a joint fashion using few-shot examples. The embeddings from the two encoders are used to compute the respective prototypes of image classes for classification. During adaptation, we propose aligning the image and text prototypes of the corresponding classes. Such alignment is beneficial for few-shot classification due to the reinforced contributions from both types of prototypes. Proto-CLIP has both training-free and fine-tuned variants. We demonstrate the effectiveness of our method by conducting experiments on benchmark datasets for few-shot learning, as well as in the real world for robot perception. The project page is available at https://irvlutd.github.io/Proto-CLIP

Ninad Khargonkar, Sai Haneesh Allu, Yangxiao Lu et al.

We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results are comparable to other studies. Additionally, the benchmark is designed to be easily reproducible in the real world, making it accessible to researchers and practitioners. We also provide our experimental results and analyzes for model-based and model-free 6D robotic grasping on the benchmark, where representative algorithms are evaluated for object perception, grasping planning, and motion planning. We believe that our benchmark will be a valuable tool for advancing the field of robot manipulation. By providing a standardized evaluation framework, researchers can more easily compare different techniques and algorithms, leading to faster progress in developing robot manipulation methods.

Jishnu Jaykumar P, Cole Salvato, Vinaya Bomnale et al.

Robotic perception models often fail when deployed in real-world environments due to out-of-distribution conditions such as clutter, occlusion, and novel object instances. Existing approaches address this gap through offline data collection and retraining, which are slow and do not resolve deployment-time failures. We propose iTeach, a failure-driven interactive teaching framework for adapting robot perception in the wild. A co-located human observes model predictions during deployment, identifies failure cases, and performs short human-object interaction (HumanPlay) to expose informative object configurations while recording RGB-D sequences. To minimize annotation effort, iTeach employs a Few-Shot Semi- Supervised (FS3) labeling strategy, where only the final frame of a short interaction sequence is annotated using hands-free eye-gaze and voice commands, and labels are propagated across the video to produce dense supervision. The collected failure-driven samples are used for iterative fine-tuning, enabling progressive deployment-time adaptation of the perception model. We evaluate iTeach on unseen object instance segmentation (UOIS) starting from a pretrained MSMFormer model. Using a small number of failure-driven samples, our method significantly improves segmentation performance across diverse real-world scenes. These improvements directly translate to higher grasping and pick-and-place success on the SceneReplica benchmark and real robotic experiments. Our results demonstrate that failure-driven, co-located interactive teaching enables efficient in-the-wild adaptation of robot perception and improves downstream manipulation performance. Project page at https://irvlutd.github.io/iTeach