OpenRaiser/Intern-Atlas huggingface dataset:
Simultaneous Pick and Place Detection by Combining SE(3) Diffusion Models with Differential Kinematics
Venue: Conference IROS 2025
Year: 2025

Grasp detection methods typically target the detection of a set of free-floating hand poses that can grasp the object. However, not all of the detected grasp poses are executable due to physical constraints. Even though it is straightforward to filter invalid grasp poses in the post-process, such a two-staged approach is computationally inefficient, especially when the constraint is hard. In this work, we propose an approach to take the following two constraints into account during the grasp detection stage, namely, (i) the picked object must be able to be placed with a predefined configuration without in-hand manipulation (ii) it must be reachable by the robot under the joint limit and collision-avoidance constraints for both pick and place cases. Our key idea is to train an SE(3) grasp diffusion network to estimate the noise in the form of spatial velocity, and constrain the denoising process by a multi-target differential inverse kinematics with an inequality constraint, so that the states are guaranteed to be reachable and placement can be performed without collision. In addition to an improved success ratio, we experimentally confirmed that our approach is more efficient and consistent in computation time compared to a naive two-stage approach.
Learning Dexterous In-Hand Manipulation with Multifingered Hands via Visuomotor Diffusion
Venue: Conference IROS 2025
Year: 2025

We present a framework for learning dexterous in-hand manipulation with multifingered hands using visuomotor diffusion policies. Our system enables complex in-hand manipulation tasks, such as unscrewing a bottle lid with one hand, by leveraging a fast and responsive teleoperation setup for the four-fingered Allegro Hand. We collect high-quality expert demonstrations using an augmented reality (AR) interface that tracks hand movements and applies inverse kinematics and motion retargeting for precise control. The AR headset provides real-time visualization, while gesture controls streamline teleoperation. To enhance policy learning, we introduce a novel demonstration outlier removal approach based on HDBSCAN clustering and the Global-Local Outlier Score from Hierarchies (GLOSH) algorithm, effectively filtering out low-quality demonstrations that could degrade performance. We evaluate our approach extensively in real-world settings and provide all experimental videos on the project website: https://dex-manip.github.io/
Manip4Care: Robotic Manipulation of Human Limbs for Solving Assistive Tasks
Venue: Conference IROS 2025
Year: 2025

Enabling robots to grasp and reposition human limbs can significantly enhance their ability to provide assistive care to individuals with severe mobility impairments, particularly in tasks such as robot-assisted bed bathing and dressing. However, existing assistive robotics solutions often assume that the human remains static or quasi-static, limiting their effectiveness. To address this issue, we present Manip4Care, a modular simulation pipeline that enables robotic manipulators to grasp and reposition human limbs effectively. Our approach features a physics simulator equipped with built-in techniques for grasping and repositioning while considering biomechanical and collision avoidance constraints. Our grasping method employs antipodal sampling with force closure to grasp limbs, and our repositioning system utilizes the Model Predictive Path Integral (MPPI) and vector-field-based control method to generate motion trajectories under collision avoidance and biomechanical constraints. We evaluate this approach across various limb manipulation tasks in both supine and sitting positions and compare outcomes for different age groups with differing shoulder joint limits. Additionally, we demonstrate our approach for limb manipulation using a real-world mannequin and further showcase its effectiveness in bed bathing tasks.
The Constitutional Filter: Bayesian Estimation of Compliant Agents
Venue: Conference IROS 2025
Year: 2025

Predicting agents impacted by legal policies, physical limitations, and operational preferences is inherently difficult. In recent years, neuro-symbolic methods have emerged, integrating machine learning and symbolic reasoning models into end-to-end learnable systems. Hereby, a promising avenue for expressing high-level constraints over multi-modal input data in robotics has opened up. This work introduces an approach for Bayesian estimation of agents expected to comply with a human-interpretable neuro-symbolic model we call its Constitution. Hence, we present the Constitutional Filter (CoFi), leading to improved tracking of agents by leveraging expert knowledge, incorporating deep learning architectures, and accounting for environmental uncertainties. CoFi extends the general, recursive Bayesian estimation setting, ensuring compatibility with a vast landscape of established techniques such as Particle Filters. To underpin the advantages of CoFi, we evaluate its performance on real-world marine traffic data. Beyond improved performance, we show how CoFi can learn to trust and adapt to the level of compliance of an agent, recovering baseline performance even if the assumed Constitution clashes with reality.
AutoSpatial: Visual-Language Reasoning for Social Robot Navigation through Efficient Spatial Reasoning Learning
Venue: Conference IROS 2025
Year: 2025

We present a novel method, AutoSpatial, an efficient approach with structured spatial grounding to enhance VLMs' spatial reasoning. By combining minimal manual supervision with large-scale Visual Question-Answering (VQA) pairs auto-labeling, our approach tackles the challenge of VLMs' limited spatial understanding in social navigation tasks. By applying a hierarchical two-round VQA strategy during training, AutoSpatial achieves both global and detailed understanding of scenarios, demonstrating more accurate spatial perception, movement prediction, Chain of Thought (CoT) reasoning, final action, and explanation compared to other SOTA approaches. These five components are essential for comprehensive social navigation reasoning. Our approach was evaluated using both expert systems (GPT-4o, Gemini 2.0 Flash, and Claude 3.5 Sonnet) that provided cross-validation scores and human evaluators who assigned relative rankings to compare model performances across four key aspects. Augmented by the enhanced spatial reasoning capabilities, AutoSpatial demonstrates substantial improvements by averaged cross-validation score from expert systems in: perception & prediction (up to 10.71%), reasoning (up to 16.26%), action (up to 20.50%), and explanation (up to 18.73%) compared to baseline models trained only on manually annotated data.
Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory
Venue: Conference IROS 2025
Year: 2025

To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progresses, the amount of data fitted by the AD model expands, which helps to improve the AD model generalization substantially. However, such ever-expanding data is a double-edged sword for the AD model. Specifically, as the fitted data volume grows to exceed the the AD model's fitting capacities, the AD model is prone to under-fitting. To address this issue, we propose to use a pretrained Large Vision Models (LVMs) as backbone coupled with downstream perception head to understand AD semantic information. This design can not only surmount the aforementioned under-fitting problem due to LVMs' powerful fitting capabilities, but also enhance the perception generalization thanks to LVMs' vast and diverse training data. On the other hand, to mitigate vehicles' computational burden of training the perception head while running LVM backbone, we introduce a Posterior Optimization Trajectory (POT)-Guided optimization scheme (POTGui) to accelerate the convergence. Concretely, we propose a POT Generator (POTGen) to generate posterior (future) optimization direction in advance to guide the current optimization iteration, through which the model can generally converge within 10 epochs. Extensive experiments demonstrate that the proposed method improves the performance by over 66.48\% and converges faster over 6 times, compared to the existing state-of-the-art approach.
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
Venue: Conference IROS 2025
Year: 2025

Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization due to domain-shift. Federated Learning (FL) has emerged as a promising paradigm for enhancing the generalization of AD models through privacy-preserving distributed learning. However, these FL AD models face significant temporal catastrophic forgetting when deployed in dynamically evolving environments, where continuous adaptation causes abrupt erosion of historical knowledge. This paper proposes Federated Exponential Moving Average (FedEMA), a novel framework that addresses this challenge through two integral innovations: (I) Server-side model's historical fitting capability preservation via fusing current FL round's aggregation model and a proposed previous FL round's exponential moving average (EMA) model; (II) Vehicle-side negative entropy regularization to prevent FL models' possible overfitting to EMA-introduced temporal patterns. Above two strategies empower FedEMA a dual-objective optimization that balances model generalization and adaptability. In addition, we conduct theoretical convergence analysis for the proposed FedEMA. Extensive experiments both on Cityscapes dataset and Camvid dataset demonstrate FedEMA's superiority over existing approaches, showing 7.12% higher mean Intersection-over-Union (mIoU).
Achieving Precise and Reliable Locomotion with Differentiable Simulation-Based System Identification
Venue: Conference IROS 2025
Year: 2025

Accurate system identification is crucial for reducing trajectory drift in bipedal locomotion, particularly in reinforcement learning and model-based control. In this paper, we present a novel control framework that integrates system identification into the reinforcement learning training loop using differentiable simulation. Unlike traditional approaches that rely on direct torque measurements, our method estimates system parameters using only trajectory data (positions, velocities) and control inputs. We leverage the differentiable simulator MuJoCo-XLA to optimize system parameters, ensuring that simulated robot behavior closely aligns with real-world motion. This framework enables scalable and flexible parameter optimization. Accurate system identification is crucial for reducing trajectory drift in bipedal locomotion, particularly in reinforcement learning and model-based control. In this paper, we present a novel control framework that integrates system identification into the reinforcement learning training loop using differentiable simulation. Unlike traditional approaches that rely on direct torque measurements, our method estimates system parameters using only trajectory data (positions, velocities) and control inputs. We leverage the differentiable simulator MuJoCo-XLA to optimize system parameters, ensuring that simulated robot behavior closely aligns with real-world motion. This framework enables scalable and flexible parameter optimization. It supports fundamental physical properties such as mass and inertia. Additionally, it handles complex system nonlinear behaviors, including advanced friction models, through neural network approximations. Experimental results show that our framework significantly improves trajectory following.
ResLPR: A LiDAR Data Restoration Network and Benchmark for Robust Place Recognition Against Weather Corruptions
Venue: Conference IROS 2025
Year: 2025

LiDAR-based place recognition (LPR) is a key component for autonomous driving, and its resilience to environmental corruption is critical for safety in high-stakes applications. While state-of-the-art (SOTA) LPR methods perform well in clean weather, they still struggle with weather-induced corruption commonly encountered in driving scenarios. To tackle this, we propose ResLPRNet, a novel LiDAR data restoration network that largely enhances LPR performance under adverse weather by restoring corrupted LiDAR scans using a wavelet transform-based network. ResLPRNet is efficient, lightweight and can be integrated plug-and-play with pretrained LPR models without substantial additional computational cost. Given the lack of LPR datasets under adverse weather, we introduce ResLPR, a novel benchmark that examines SOTA LPR methods under a wide range of LiDAR distortions induced by severe snow, fog, and rain conditions. Experiments on our proposed WeatherKITTI and WeatherNCLT datasets demonstrate the resilience and notable gains achieved by using our restoration method with multiple LPR approaches in challenging weather scenarios. Our code and benchmark are publicly available here: https://github.com/nubot-nudt/ResLPR.
Towards Design and Development of a Concentric Tube Steerable Drilling Robot for Creating S-shape Tunnels for Pelvic Fixation Procedures
Venue: Conference IROS 2025
Year: 2025

Current pelvic fixation techniques rely on rigid drilling tools, which inherently constrain the placement of rigid medical screws in the complex anatomy of pelvis. These constraints prevent medical screws from following anatomically optimal pathways and force clinicians to fixate screws in linear trajectories. This suboptimal approach, combined with the unnatural placement of the excessively long screws, lead to complications such as screw misplacement, extended surgery times, and increased radiation exposure due to repeated X-ray images taken ensure to safety of procedure. To address these challenges, in this paper, we present the design and development of a unique 4 degree-of-freedom (DoF) pelvic concentric tube steerable drilling robot (pelvic CT-SDR). The pelvic CT-SDR is capable of creating long S-shaped drilling trajectories that follow the natural curvatures of the pelvic anatomy. The performance of the pelvic CT-SDR was thoroughly evaluated through several S-shape drilling experiments in simulated bone phantoms.
Augmented Bridge Spinal Fixation: A New Concept for Addressing Pedicle Screw Pullout via a Steerable Drilling Robot and Flexible Pedicle Screws
Venue: Conference IROS 2025
Year: 2025

To address the screw loosening and pullout limitations of rigid pedicle screws in spinal fixation procedures, and to leverage our recently developed Concentric Tube Steerable Drilling Robot (CT-SDR) and Flexible Pedicle Screw (FPS), in this paper, we introduce the concept of Augmented Bridge Spinal Fixation (AB-SF). In this concept, two connecting J-shape tunnels are first drilled through pedicles of vertebra using the CT-SDR. Next, two FPSs are passed through this tunnel and bone cement is then injected through the cannulated region of the FPS to form an augmented bridge between two pedicles and reinforce strength of the fixated spine. To experimentally analyze and study the feasibility of AB-SF technique, we first used our robotic system (i.e., a CT-SDR integrated with a robotic arm) to create two different fixation scenarios in which two J-shape tunnels, forming a bridge, were drilled at different depth of a vertebral phantom. Next, we implanted two FPSs within the drilled tunnels and then successfully simulated the bone cement augmentation process.
Parallel Transmission Aware Co-Design: Enhancing Manipulator Performance Through Actuation-Space Optimization
Venue: Conference IROS 2025
Year: 2025

In robotics, structural design and behavior optimization have long been considered separate processes, resulting in the development of systems with limited capabilities. Recently, co-design methods have gained popularity, where bi-level formulations are used to simultaneously optimize the robot design and behavior for specific tasks. However, most implementations assume a serial or tree-type model of the robot, overlooking the fact that many robot platforms incorporate parallel mechanisms. In this paper, we present a novel co-design approach that explicitly incorporates parallel coupling constraints into the dynamic model of the robot. In this framework, an outer optimization loop focuses on the design parameters, in our case the transmission ratios of a parallel belt-driven manipulator, which map the desired torques from the joint space to the actuation space. An inner loop performs trajectory optimization in the actuation space, thus exploiting the entire dynamic range of the manipulator. We compare the proposed method with a conventional co-design approach based on a simplified tree-type model. By taking advantage of the actuation space representation, our approach leads to a significant increase in dynamic payload capacity compared to the conventional co-design implementation.
YO-CSA-T: A Real-time Badminton Tracking System Utilizing YOLO Based on Contextual and Spatial Attention
Venue: Conference IROS 2025
Year: 2025

The 3D trajectory of a shuttlecock required for a badminton rally robot for human-robot competition demands real-time performance with high accuracy. However, the fast flight speed of the shuttlecock, along with various visual effects, and its tendency to blend with environmental elements, such as court lines and lighting, present challenges for rapid and accurate 2D detection. In this paper, we first propose the YO-CSA detection network, which optimizes and reconfigures the YOLOv8s model's backbone, neck, and head by incorporating contextual and spatial attention mechanisms to enhance model's ability in extracting and integrating both global and local features. Next, we integrate three major subtasks, detection, prediction, and compensation, into a real-time 3D shuttlecock trajectory detection system. Specifically, our system maps the 2D coordinate sequence extracted by YO-CSA into 3D space using stereo vision, then predicts the future 3D coordinates based on historical information, and re-projects them onto the left and right views to update the position constraints for 2D detection. Additionally, our system includes a compensation module to fill in missing intermediate frames, ensuring a more complete trajectory. We conduct extensive experiments on our own dataset to evaluate both YO-CSA's performance and system effectiveness. Experimental results show that YO-CSA achieves a high accuracy of 90.43% mAP@0.75, surpassing both YOLOv8s and YOLO11s. Our system performs excellently, maintaining a speed of over 130 fps across 12 test sequences.
Scalable Outdoors Autonomous Drone Flight with Visual-Inertial SLAM and Dense Submaps Built without LiDAR
Venue: Conference IROS 2025
Year: 2025

Autonomous navigation is needed for several robotics applications. In this paper we present an autonomous Micro Aerial Vehicle (MAV) system which purely relies on cost-effective and light-weight passive visual and inertial sensors to perform large-scale autonomous navigation in outdoor,unstructured and cluttered environments. We leverage visual-inertial simultaneous localization and mapping (VI-SLAM) for accurate MAV state estimates and couple it with a volumetric occupancy submapping system to achieve a scalable mapping framework which can be directly used for path planning. To ensure the safety of the MAV during navigation, we also propose a novel reference trajectory anchoring scheme that deforms the reference trajectory the MAV is tracking upon state updates from the VI-SLAM system in a consistent way, even upon large state updates due to loop-closures. We thoroughly validate our system in both real and simulated forest environments and at peak velocities up to 3 m/s while not encountering a single collision or system failure. To the best of our knowledge, this is the first system which achieves this level of performance in such an unstructured environment using low-cost passive visual sensors and fully on-board computation, including VI-SLAM.
Leveraging Temporally Extended Behavior Sharing for Multi-task Reinforcement Learning
Venue: Conference IROS 2025
Year: 2025

Multi-task reinforcement learning (MTRL) offers a promising approach to improve sample efficiency and generalization by training agents across multiple tasks, enabling knowledge sharing between them. However, applying MTRL to robotics remains challenging due to the high cost of collecting diverse task data. To address this, we propose MT-Lévy, a novel exploration strategy that enhances sample efficiency in MTRL environments by combining behavior sharing across tasks with temporally extended exploration inspired by Lévy flight. MT-Lévy leverages policies trained on related tasks to guide exploration towards key states, while dynamically adjusting exploration levels based on task success ratios. This approach enables more efficient state-space coverage, even in complex robotics environments. Empirical results demonstrate that MT-Lévy significantly improves exploration and sample efficiency, supported by quantitative and qualitative analyses. Ablation studies further highlight the contribution of each component, showing that combining behavior sharing with adaptive exploration strategies can significantly improve the practicality of MTRL in robotics applications.
Scanning Bot: Efficient Scan Planning using Panoramic Cameras
Venue: Conference IROS 2025
Year: 2025

Panoramic RGB-D cameras are known for their ability to produce high quality 3D scene reconstructions. However, operating these cameras involves manually selecting viewpoints and physically transporting the camera, making the generation of a 3D model time consuming and tedious. Additionally, the process can be challenging for novice users due to spatial constraints, such as ensuring sufficient feature overlap between viewpoint frames. To address these challenges, we propose a fully autonomous scan planning that generates an efficient tour plan for environment scanning, ensuring collision-free navigation and adequate overlap between viewpoints within the plan. Extensive experiments conducted in both synthetic and real-world environments validate the performance of our planner against state-of-the-art view planners. In particular, our method achieved an average scan coverage of 99 percent in the real-world experiment, with our approach being up to 3 times faster than state-of-the-art planners in total scan time.
Trajectory Optimization for In-Hand Manipulation with Tactile Force Control
Venue: Conference IROS 2025
Year: 2025

The strength of the human hand lies in its ability to manipulate small objects precisely and robustly. In contrast, simple robotic grippers have low dexterity and fail to handle small objects effectively. This is why many automation tasks remain unsolved by robots. This paper presents an optimization-based framework for in-hand manipulation with a robotic hand equipped with compact Magnetic Tactile Sensors (MTSs). The small form factor of the robotic hand from Shadow Robot introduces challenges in estimating the state of the object while satisfying contact constraints. To address this, we formulate a trajectory optimization problem using Nonlinear Programming (NLP) for finger movements while ensuring contact points to change along the geometry of the fingers. Using the optimized trajectory from the solver, we implement and test an open-loop controller for rolling motion. To further enhance robustness and accuracy, we introduce a force controller for the fingers and a state estimator for the object utilizing MTSs. The proposed framework is validated through comparative experiments, showing that incorporating the force control with compliance consideration improves the accuracy and robustness of the rolling motion. Rolling an object with the force controller is 30\% more likely to succeed than running an open-loop controller. The demonstration video is available at https://youtu.be/6J_muL_AyE8.