Paper 1
■書誌情報
Jeremy Siburian, Cristian Camilo Beltran-Hernandez, Tatsuya Matsushima, Yusuke Iwasawa, Masashi Hamaya, Mai Nishimura: PHASE: Compliance-Enabled Tactile Phase Retrieval for Few-Shot Insertion Learning, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026), September 2026
■概要
Contact-rich assembly tasks such as peg-in-hole insertion remain difficult to learn from limited demonstrations. While retrieval-augmented imitation learning, which augments target demonstrations with relevant prior data, offers a promising direction, its applicability to contact-rich manipulation remains largely unexplored. Contact-rich insertion unfolds over multiple phases from search to insert, and retrieving phase-specific experience from prior data in principled ways remains an open question. Our key insight is that a compliant wrist enables the robot to sustain contact throughout execution, producing rich tactile and force signals that naturally reveal the phase structure of insertion and inform what should be retrieved. Based on this insight, we present PHASE (PHase-Aware Segmentation and REtrieval), a framework for compliance-enabled tactile phase retrieval that integrates multimodal contact-aware representation learning, variable-length phase segmentation from tactile signals, and phase consistent retrieval for policy learning. We evaluate PHASE on real-world peg-in-hole insertion across five peg geometries, comparing against retrieval strategies drawn from state-of-the-art methods under a shared policy architecture. PHASE outperforms all baselines that do not account for phase structure by +13% or more on seen and unseen geometries, and by +30% under state distribution shift. These results demonstrate that aligning retrieval with interaction defined contact phases substantially improves robustness in few-shot insertion learning.
Paper 2
■書誌情報
Hisayuki Yokomizo, Taiki Miyanishi, Gang Yan, Shuhei Kurita, Nakamasa Inoue, Yusuke Iwasawa: PhysQuantAgent: An Inference Pipeline of Mass Estimation for Vision-Language Models, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026), September 2026
■概要
Vision-Language Models (VLMs) are increasingly applied to robotic perception and manipulation, yet their ability to infer physical properties required for manipulation remains limited. In particular, estimating the mass of real-world objects is essential for determining appropriate grasp force and ensuring safe interaction. However, current VLMs lack reliable mass reasoning capabilities, and most existing benchmarks do not explicitly evaluate physical quantity estimation under realistic sensing conditions. In this work, we propose PhysQuantAgent, a framework for real-world object mass estimation using VLMs, together with VisPhysQuant, a new benchmark dataset for evaluation. VisPhysQuant consists of RGB-D videos of real objects captured from multiple viewpoints, annotated with precise mass measurements. To improve estimation accuracy, we introduce three visual prompting methods that enhance the input image with object detection, scale estimation, and cross-sectional image generation to help the model comprehend the size and internal structure of the target object. Experiments show that visual prompting significantly improves mass estimation accuracy on real-world data, suggesting the efficacy of integrating spatial reasoning with VLM knowledge for physical inference.
