Thegoal targetobjectisinthefridge. 4000 steps 3. Set Table: Getabowl (see Fig.19c).

Perceiving and manipulating 3D articulated objects in diverse environments is essential for home-assistant robots.

In this paper, we take a step toward narrowing the gap between model-based and model-free RL methods.

Social Robot Navigation is the skill that allows robots to move efficiently in human-populated environments while ensuring safety, comfort, and trust. Unlike other areas of research, the scientific community has not yet achieved an agreement on how Social Robot Navigation should be benchmarked. This is notably important, as the lack of a de facto standard to benchmark Social Robot Navigation can hinder the progress of the field and may lead to contradicting conclusions. Motivated by this gap, we contribute with a short review focused exclusively on benchmarking trends in the period from January 2020 to July 2025. Of the 130 papers identified by our search using IEEE Xplore, we analysed the 85 papers that met the criteria of the review. This review addresses the metrics used in the literature for benchmarking purposes, the algorithms employed in such benchmarks, the use of human surveys for benchmarking, and how conclusions are drawn from the benchmarking results, when applicable.

The rapidly growing number of product categories in large-scale e-commerce makes accurate object identification for automated packing in warehouses substantially more difficult. As the catalog grows, intra-class variability and a long tail of rare or visually similar items increase, and when combined with diverse packaging, cluttered containers, frequent occlusion, and large viewpoint changes-these factors amplify discrepancies between query and reference images, causing sharp performance drops for methods that rely solely on 2D appearance features. Thus, we propose RoboEye, a two-stage identification framework that dynamically augments 2D semantic features with domain-adapted 3D reasoning and lightweight adapters to bridge training deployment gaps. In the first stage, we train a large vision model to extract 2D features for generating candidate rankings. A lightweight 3D-feature-awareness module then estimates 3D feature quality and predicts whether 3D re-ranking is necessary, preventing performance degradation and avoiding unnecessary computation. When invoked, the second stage uses our robot 3D retrieval transformer, comprising a 3D feature extractor that produces geometry-aware dense features and a keypoint-based matcher that computes keypoint-correspondence confidences between query and reference images instead of conventional cosine-similarity scoring. Experiments show that RoboEye improves Recall@1 by 7.1% over the prior state of the art (RoboLLM). Moreover, RoboEye operates using only RGB images, avoiding reliance on explicit 3D inputs and reducing deployment costs. The code used in this paper is publicly available at: https://github.com/longkukuhi/RoboEye.

We consider manipulation problems in constrained and cluttered settings, which require several regrasps at unknown locations. We propose to inform an optimization-based task and motion planning (TAMP) solver with possible regrasp areas and grasp sequences to speed up the search. Our main idea is to use a state space abstraction, a regrasp map, capturing the combinations of available grasps in different parts of the configuration space, and allowing us to provide the solver with guesses for the mode switches and additional constraints for the object placements. By interleaving the creation of regrasp maps, their adaptation based on failed refinements, and solving TAMP (sub)problems, we are able to provide a robust search method for challenging regrasp manipulation problems.

-- This work presents KnotDLO, a method for one-handed Deformable Linear Object (DLO) knot tying that is robust to occlusion, repeatable for varying rope initial configurations, interpretable for generating motion policies, and requires no human demonstrations or training. Grasp and target waypoints for future DLO states are planned from the current DLO shape. Grasp poses are computed from indexing the tracked piecewise linear curve representing the DLO state based on the current curve shape and are piecewise continuous. KnotDLO computes intermediate waypoints from the geometry of the current DLO state and the desired next state. In 16 trials of knot tying, KnotDLO achieves a 50% success rate in tying an overhand knot from previously unseen configurations.

Since the rise of ChatGPT, generative artificial intelligence (GenAI) technologies gained widespread popularity, impacting academic research and everyday communication.5,10 While GenAI offers benefits in task automation,9 it can also be misused and abused in nefarious applications,7 with significant risks to long-tail populations.6 Professionals in fields such as journalism and law still remain cautious due to concerns related to hallucinations and ethical issues, but scholars in computer science (CS), the field where GenAI originated, appear to be cautiously, yet actively exploring its use. For instance, Liang, W. et al.3 report the increased use of large language models (LLMs) in the CS scholarly articles (up to 17.5%), compared to mathematics articles (up to 6.3%), and Liang, W. et al.2 report that, between 6.5% and 16.9% of peer reviews at ICLR 2024, NeurIPS 2023, CoRL 2023, and EMNLP 2023 may have been altered by LLMs beyond minor revisions. Considering researchers' increasing adoption of GenAI, it is crucial to establish usage policies to promote fair and ethical practices in scholarly writing and peer reviews.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we learn about drug and material design using generative models and Bayesian optimization, find out about a system for real-time monitoring for healthcare data, and explore domain-specific distribution shifts in volunteer-collected biodiversity datasets. Ananya Joshi recently completed her PhD, where she developed a system that experts have used for the past two years to identify respiratory outbreaks (like COVID-19) in large-scale healthcare streams across the United States. In this interview, she tells us more about this project, how healthcare applications inspire basic AI research, and her future plans. Onur Boyar is a PhD student at Nagoya university, working on generative models and Bayesian methods for materials and drug design.

A prior map serves as a foundational reference for localization in context-aware applications such as augmented reality (AR). Providing valuable contextual information about the environment, the prior map is a vital tool for mitigating drift. In this paper, we propose a map-based visual-inertial localization algorithm (NeRF-VIO) with initialization using neural radiance fields (NeRF). Our algorithm utilizes a multilayer perceptron model and redefines the loss function as the geodesic distance on \(SE(3)\), ensuring the invariance of the initialization model under a frame change within \(\mathfrak{se}(3)\). The evaluation demonstrates that our model outperforms existing NeRF-based initialization solution in both accuracy and efficiency. By integrating a two-stage update mechanism within a multi-state constraint Kalman filter (MSCKF) framework, the state of NeRF-VIO is constrained by both captured images from an onboard camera and rendered images from a pre-trained NeRF model. The proposed algorithm is validated using a real-world AR dataset, the results indicate that our two-stage update pipeline outperforms MSCKF across all data sequences.