OpenRaiser/Intern-Atlas huggingface dataset:
Modular Soft Wearable Glove for Real-Time Gesture Recognition and Dynamic 3D Shape Reconstruction
Venue: Conference IROS 2025
Year: 2025

With the increasing demand for human-computer interaction (HCI), flexible wearable gloves have emerged as a promising solution in virtual reality, medical rehabilitation, and industrial automation. However, the current technology still has problems like insufficient sensitivity and limited durability, which hinder its wide application. This paper presents a highly sensitive, modular, and flexible capacitive sensor based on line-shaped electrodes and liquid metal (EGaIn), integrated into a sensor module tailored to the human hand's anatomy. The proposed system independently captures bending information from each finger joint, while additional measurements between adjacent fingers enable the recording of subtle variations in inter-finger spacing. This design enables accurate gesture recognition and dynamic hand morphological reconstruction of complex movements using point clouds. Experimental results demonstrate that our classifier based on Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) achieves an accuracy of 99.15% across 30 gestures. Meanwhile, a transformer-based Deep Neural Network (DNN) accurately reconstructs dynamic hand shapes with an Average Distance (AD) of 2.076\pm3.231 mm, with the reconstruction accuracy at individual key points surpassing SOTA benchmarks by 9.7% to 64.9%. The proposed glove shows excellent accuracy, robustness and scalability in gesture recognition and hand reconstruction, making it a promising solution for next-generation HCI systems.
Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering
Venue: Conference IROS 2025
Year: 2025

We present and tackle the problem of Embodied Question Answering (EQA) with Situational Queries (S-EQA) in a household environment. Unlike prior EQA work tackling simple queries that directly reference target objects and properties ("What is the color of the car?"), situational queries (such as "Is the house ready for sleeptime?") are challenging as they require the agent to correctly identify multiple object-states (Doors: Closed, Lights: Off, etc.) and reach a consensus on their states for an answer. Towards this objective, we first introduce a novel Prompt-Generate-Evaluate (PGE) scheme that wraps around an LLM's output to generate unique situational queries and corresponding consensus object information. PGE is used to generate 2K datapoints in the VirtualHome simulator, which is then annotated for ground truth answers via a large scale user-study conducted on M-Turk. With a high rate of answerability (97.26%) on this study, we establish that LLMs are good at generating situational data. However, in evaluating the data using an LLM, we observe a low correlation of 46.2% with the ground truth human annotations; indicating that while LLMs are good at generating situational data, they struggle to answer them according to consensus. When asked for reasoning, we observe the LLM often goes against commonsense in justifying its answer. Finally, we utilize PGE to generate situational data in a real-world environment, exposing LLM hallucination in generating reliable object-states when a structured scene graph is unavailable. To the best of our knowledge, this is the first work to introduce EQA in the context of situational queries and also the first to present a generative approach for query creation. We aim to foster research on improving the real-world usability of embodied agents through this work.
Task-driven SLAM Benchmarking for Robot Navigation
Venue: Conference IROS 2025
Year: 2025

A critical use case of SLAM for mobile assistive robots is to support localization during a navigation-based task. Current SLAM benchmarks overlook the significance of repeatability (precision), despite its importance in real-world deployments. To address this gap, we propose a task-driven approach to SLAM benchmarking, TaskSLAM-Bench. It employs precision as a key metric, accounts for SLAM's mapping capabilities, and has easy-to-meet implementation requirements. Simulated and real-world testing scenarios of SLAM methods provide insights into the navigation performance properties of modern visual and LiDAR SLAM solutions. The outcomes show that passive stereo SLAM operates at a level of precision comparable to LiDAR SLAM in typical indoor environments. TaskSLAM-Bench complements existing benchmarks and offers richer assessment of SLAM performance in navigation-focused scenarios. Publicly available code permits in-situ SLAM testing in custom environments with properly equipped robots.
Real-time Iteration Scheme for Diffusion Policy
Venue: Conference IROS 2025
Year: 2025

Diffusion Policies have demonstrated impressive performance in robotic manipulation tasks. However, their long inference time, resulting from an extensive iterative denoising process, and the need to execute an action chunk before the next prediction to maintain consistent actions limit their applicability to latency-critical tasks or simple tasks with a short cycle time. While recent methods explored distillation or alternative policy structures to accelerate inference, these often demand additional training, which can be resource-intensive for large robotic models. In this paper, we introduce a novel approach inspired by the Real-Time Iteration (RTI) Scheme, a method from optimal control that accelerates optimization by leveraging solutions from previous time steps as initial guesses for subsequent iterations. We explore the application of this scheme in diffusion inference and propose a scaling-based method to effectively handle discrete actions, such as grasping, in robotic manipulation. The proposed scheme significantly reduces runtime computational costs without the need for distillation or policy redesign. This enables a seamless integration into many pre-trained diffusion-based models, in particular, to resource-demanding large models. We also provide theoretical conditions for the contractivity which could be useful for estimating the initial denoising step. Quantitative results from extensive simulation experiments show a substantial reduction in inference time, with comparable overall performance compared with Diffusion Policy using full-step denoising. Our project page with additional resources is available at: https://rti-dp.github.io/.
Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion
Venue: Conference IROS 2025
Year: 2025

Quadrupedal animals can perform agile and playful tasks while interacting with real-world objects. For instance, a trained dog can track and catch a flying frisbee before it touches the ground, while a cat left alone at home may leap to grasp the door handle. Successfully grasping an object during high-dynamic locomotion requires highly precise perception and control. However, due to hardware limitations, agility and precision are usually a trade-off in robotics problems. In this work, we employ a perception-control decoupled system based on Reinforcement Learning (RL), aiming to explore the level of precision a quadrupedal robot can achieve while interacting with objects during high-dynamic locomotion. Our experiments show that our quadrupedal robot, mounted with a passive gripper in front of the robot's chassis, can perform both tracking and catching tasks similar to a real trained dog. The robot can follow a mid-air ball moving at speeds of up to 3m/s and it can leap and successfully catch a small object hanging above it at a height of 1.05m in simulation and 0.8m in the real world.
Measuring Uncertainty in Shape Completion to Improve Grasp Quality
Venue: Conference IROS 2025
Year: 2025

Shape completion networks have been used recently in real-world robotic experiments to complete the missing/hidden information in environments where objects are only observed in one or few instances where self-occlusions are bound to occur. Nowadays, most approaches rely on deep neural networks that handle rich 3D point cloud data that lead to more precise and realistic object geometries. However, these models still suffer from inaccuracies due to its nondeterministic/stochastic inferences which could lead to poor performance in grasping scenarios where these errors compound to unsuccessful grasps. We present an approach to calculate the uncertainty of a 3D shape completion model during inference of single view point clouds of an object on a table top. In addition, we propose an update to grasp pose algorithms quality score by introducing the uncertainty of the completed point cloud present in the grasp candidates. To test our full pipeline we perform real world grasping with a 7dof robotic arm with a 2 finger gripper on a large set of household objects and compare against previous approaches that do not measure uncertainty. Our approach ranks the grasp quality better, leading to higher grasp success rate for the rank 5 grasp candidates compared to state of the art.
ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments
Venue: Conference IROS 2025
Year: 2025

The growing use of service robots in dynamic environments requires flexible management of on-board compute resources to optimize the performance of diverse tasks such as navigation, localization, and perception. Current robot deployments often rely on static OS configurations and system over-provisioning. However, they are suboptimal because they do not account for variations in resource usage. This results in poor system-wide behavior such as robot instability or inefficient resource use. This paper presents ConifgBot, a novel system designed to adaptively reconfigure robot applications to meet a predefined performance specification by leveraging \emph{runtime profiling} and \emph{automated configuration tuning}. Through experiments on multiple real robots, each running a different stack with diverse performance requirements, which could be \emph{context}-dependent, we illustrate ConifgBot's efficacy in maintaining system stability and optimizing resource allocation. Our findings highlight the promise of automatic system configuration tuning for robot deployments, including adaptation to dynamic changes.
Efficient Collision Detection for Long and Slender Robotic Links in Euclidean Distance Fields: Application to a Forestry Crane
Venue: Conference IROS 2025
Year: 2025

Collision-free motion planning in complex outdoor environments relies heavily on perceiving the surroundings through exteroceptive sensors. A widely used approach represents the environment as a voxelized Euclidean distance field, where robots are typically approximated by spheres. However, for large-scale manipulators such as forestry cranes, which feature long and slender links, this conventional spherical approximation becomes inefficient and inaccurate. This work presents a novel collision detection algorithm specifically designed to exploit the elongated structure of such manipulators, significantly enhancing the computational efficiency of motion planning algorithms. Unlike traditional sphere decomposition methods, our approach not only improves computational efficiency but also naturally eliminates the need to fine-tune the approximation accuracy as an additional parameter. We validate the algorithm's effectiveness using real-world LiDAR data from a forestry crane application, as well as simulated environment data.
Category-level Meta-learned NeRF Priors for Efficient Object Mapping
Venue: Conference IROS 2025
Year: 2025

In 3D object mapping, category-level priors enable efficient object reconstruction and canonical pose estimation, requiring only a single prior per semantic category (e.g., chair, book, laptop, etc.). DeepSDF has been used predominantly as a category-level shape prior, but it struggles to reconstruct sharp geometry and is computationally expensive. In contrast, NeRFs capture fine details but have yet to be effectively integrated with category-level priors in a real-time multi-object mapping framework. To bridge this gap, we introduce PRENOM, a Prior-based Efficient Neural Object Mapper that integrates category-level priors with object-level NeRFs to enhance reconstruction efficiency and enable canonical object pose estimation. PRENOM gets to know objects on a first-name basis by meta-learning on synthetic reconstruction tasks generated from open-source shape datasets. To account for object category variations, it employs a multi-objective genetic algorithm to optimize the NeRF architecture for each category, balancing reconstruction quality and training time. Additionally, prior-based probabilistic ray sampling directs sampling toward expected object regions, accelerating convergence and improving reconstruction quality under constrained resources. Experimental results highlight the ability of PRENOM to achieve high-quality reconstructions while maintaining computational feasibility. Specifically, comparisons with prior-free NeRF-based approaches on a synthetic dataset show a 21\% lower Chamfer distance. Furthermore, evaluations against other approaches using shape priors on a noisy real-world dataset indicate a 13\% improvement averaged across all reconstruction metrics, and comparable pose and size estimation accuracy, while being trained for 5$\times$ less time. Code available at: https://github.com/snt-arg/PRENOM
Haptic-Informed ACT with a Soft Gripper and Recovery-Informed Training for Pseudo Oocyte Manipulation
Venue: Conference IROS 2025
Year: 2025

In this paper, we introduce Haptic-Informed ACT, an advanced robotic system for pseudo oocyte manipulation, integrating multimodal information and Action Chunking with Transformers (ACT). Traditional automation methods for oocyte transfer rely heavily on visual perception, often requiring human supervision due to biological variability and environmental disturbances. Haptic-Informed ACT enhances ACT by incorporating haptic feedback, enabling real-time grasp failure detection and adaptive correction. Additionally, we introduce a 3D-printed TPU soft gripper to facilitate delicate manipulations. Experimental results demonstrate that Haptic-Informed ACT improves the task success rate, robustness, and adaptability compared to conventional ACT, particularly in dynamic environments. These findings highlight the potential of multimodal learning in robotics for biomedical automation.
Disentangled Object-Centric Image Representation for Robotic Manipulation
Venue: Conference IROS 2025
Year: 2025

Learning robotic manipulation skills from vision is a promising approach for developing robotics applications that can generalize broadly to real-world scenarios. As such, many approaches to enable this vision have been explored with fruitful results. Particularly, object-centric representation methods have been shown to provide better inductive biases for skill learning, leading to improved performance and generalization. Nonetheless, we show that object-centric methods can struggle to learn simple manipulation skills in multi-object environments. Thus, we propose DOCIR, an object-centric framework that introduces a disentangled representation for objects of interest, obstacles, and robot embodiment. We show that this approach leads to state-of-the-art performance for learning pick and place skills from visual inputs in multi-object environments and generalizes at test time to changing objects of interest and distractors in the scene. Furthermore, we show its efficacy both in simulation and zero-shot transfer to the real world.
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Venue: Conference IROS 2025
Year: 2025

Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360° field of view. Camera-based setups offer a cost-effective option by using stereo depth estimation to generate dense, high-resolution depth maps without relying on expensive active sensing. However, existing omnidirectional stereo matching approaches achieve only limited depth accuracy across diverse environments, depth ranges, and lighting conditions, due to the scarcity of real-world data. We present DFI-OmniStereo, a novel omnidirectional stereo matching method that leverages a large-scale pre-trained foundation model for relative monocular depth estimation within an iterative optimization-based stereo matching architecture. We introduce a dedicated two-stage training strategy to utilize the relative monocular depth features for our omnidirectional stereo matching before scale-invariant fine-tuning. DFI-OmniStereo achieves state-of-the-art results on the real-world Helvipad dataset, reducing disparity MAE by approximately 16% compared to the previous best omnidirectional stereo method.
Real-Time Manipulation Action Recognition with a Factorized Graph Sequence Encoder
Venue: Conference IROS 2025
Year: 2025

Recognition of human manipulation actions in real-time is essential for safe and effective human-robot interaction and collaboration. The challenge lies in developing a model that is both lightweight enough for real-time execution and capable of generalization. While some existing methods in the literature can run in real-time, they struggle with temporal scalability, i.e., they fail to adapt to long-duration manipulations effectively. To address this, leveraging the generalizable scene graph representations, we propose a new Factorized Graph Sequence Encoder network that not only runs in real-time but also scales effectively in the temporal dimension, thanks to its factorized encoder architecture. Additionally, we introduce Hand Pooling operation, a simple pooling operation for more focused extraction of the graph-level embeddings. Our model outperforms the previous state-of-the-art real-time approach, achieving a 14.3\% and 5.6\% improvement in F1-macro score on the KIT Bimanual Action (Bimacs) Dataset and Collaborative Action (CoAx) Dataset, respectively. Moreover, we conduct an extensive ablation study to validate our network design choices. Finally, we compare our model with its architecturally similar RGB-based model on the Bimacs dataset and show the limitations of this model in contrast to ours on such an object-centric manipulation dataset.
Efficient Human-Aware Task Allocation for Multi-Robot Systems in Shared Environments
Venue: Conference IROS 2025
Year: 2025

Multi-robot systems are increasingly deployed in applications, such as intralogistics or autonomous delivery, where multiple robots collaborate to complete tasks efficiently. One of the key factors enabling their efficient cooperation is Multi-Robot Task Allocation (MRTA). Algorithms solving this problem optimize task distribution among robots to minimize the overall execution time. In shared environments, apart from the relative distance between the robots and the tasks, the execution time is also significantly impacted by the delay caused by navigating around moving people. However, most existing MRTA approaches are dynamics-agnostic, relying on static maps and neglecting human motion patterns, leading to inefficiencies and delays. In this paper, we introduce \acrfull{method name}. This method leverages Maps of Dynamics (MoDs), spatio-temporal queryable models designed to capture historical human movement patterns, to estimate the impact of humans on the task execution time during deployment. \acrshort{method name} utilizes a stochastic cost function that includes MoDs. Experimental results show that integrating MoDs enhances task allocation performance, resulting in reduced mission completion times by up to $26\%$ compared to the dynamics-agnostic method and up to $19\%$ compared to the baseline. This work underscores the importance of considering human dynamics in MRTA within shared environments and presents an efficient framework for deploying multi-robot systems in environments populated by humans.
MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping
Venue: Conference IROS 2025
Year: 2025

Robotic grasping faces challenges in adapting to objects with varying shapes and sizes. In this paper, we introduce MISCGrasp, a volumetric grasping method that integrates multi-scale feature extraction with contrastive feature enhancement for self-adaptive grasping. We propose a query-based interaction between high-level and low-level features through the Insight Transformer, while the Empower Transformer selectively attends to the highest-level features, which synergistically strikes a balance between focusing on fine geometric details and overall geometric structures. Furthermore, MISCGrasp utilizes multi-scale contrastive learning to exploit similarities among positive grasp samples, ensuring consistency across multi-scale features. Extensive experiments in both simulated and real-world environments demonstrate that MISCGrasp outperforms baseline and variant methods in tabletop decluttering tasks. More details are available at https://miscgrasp.github.io/.
JPDS-NN: Reinforcement Learning-Based Dynamic Task Allocation for Agricultural Vehicle Routing Optimization
Venue: Conference IROS 2025
Year: 2025

The Entrance Dependent Vehicle Routing Problem (EDVRP) is a variant of the Vehicle Routing Problem (VRP) where the scale of cities influences routing outcomes, necessitating consideration of their entrances. This paper addresses EDVRP in agriculture, focusing on multi-parameter vehicle planning for irregularly shaped fields. To address the limitations of traditional methods, such as heuristic approaches, which often overlook field geometry and entrance constraints, we propose a Joint Probability Distribution Sampling Neural Network (JPDS-NN) to effectively solve the EDVRP. The network uses an encoder-decoder architecture with graph transformers and attention mechanisms to model routing as a Markov Decision Process, and is trained via reinforcement learning for efficient and rapid end-to-end planning. Experimental results indicate that JPDS-NN reduces travel distances by 48.4-65.4%, lowers fuel consumption by 14.0-17.6%, and computes two orders of magnitude faster than baseline methods, while demonstrating 15-25% superior performance in dynamic arrangement scenarios. Ablation studies validate the necessity of cross-attention and pre-training. The framework enables scalable, intelligent routing for large-scale farming under dynamic constraints.
ReBot: Scaling Robot Learning with Real-to-Sim-to-Real Robotic Video Synthesis
Venue: Conference IROS 2025
Year: 2025

Vision-language-action (VLA) models present a promising paradigm by training policies directly on real robot datasets like Open X-Embodiment. However, the high cost of real-world data collection hinders further data scaling, thereby restricting the generalizability of VLAs. In this paper, we introduce ReBot, a novel real-to-sim-to-real approach for scaling real robot datasets and adapting VLA models to target domains, which is the last-mile deployment challenge in robot manipulation. Specifically, ReBot replays real-world robot trajectories in simulation to diversify manipulated objects (real-to-sim), and integrates the simulated movements with inpainted real-world background to synthesize physically realistic and temporally consistent robot videos (sim-to-real). Our approach has several advantages: 1) it enjoys the benefit of real data to minimize the sim-to-real gap; 2) it leverages the scalability of simulation; and 3) it can generalize a pretrained VLA to a target domain with fully automated data pipelines. Extensive experiments in both simulation and real-world environments show that ReBot significantly enhances the performance and robustness of VLAs. For example, in SimplerEnv with the WidowX robot, ReBot improved the in-domain performance of Octo by 7.2% and OpenVLA by 21.8%, and out-of-domain generalization by 19.9% and 9.4%, respectively. For real-world evaluation with a Franka robot, ReBot increased the success rates of Octo by 17% and OpenVLA by 20%. More information can be found at: https://yuffish.github.io/rebot/