OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Robot Programming: Mixed Reality Gripper Control
Venue: Conference ICRA 2025
Year: 2025

Conventional robot programming methods are complex and time-consuming for users. In recent years, alternative approaches such as mixed reality have been explored to address these challenges and optimize robot programming. While the findings of the mixed reality robot programming methods are convincing, most existing methods rely on gesture interaction for robot programming. Since controller-based interactions have proven to be more reliable, this paper examines three controller-based programming methods within a mixed reality scenario: 1) Classical Jogging, where the user positions the robot's end effector using the controller's thumbsticks, 2) Direct Control, where the controller's position and orientation directly corresponds to the end effector's, and 3) Gripper Control, where the controller is enhanced with a 3D-printed gripper attachment to grasp and release objects. A within-subjects study (n = 30) was conducted to compare these methods. The findings indicate that the Gripper Control condition outperforms the others in terms of task completion time, user experience, mental demand, and task performance, while also being the preferred method. Therefore, it demonstrates promising potential as an effective and efficient approach for future robot programming. Video available at https://youtu.be/83kWr8zUFIQ.
Text2Robot: Evolutionary Robot Design from Text Descriptions
Venue: Conference ICRA 2025
Year: 2025

Robot design has traditionally been costly and labor-intensive. Despite advancements in automated processes, it remains challenging to navigate a vast design space while producing physically manufacturable robots. We introduce Text2Robot, a framework that converts user text specifications and performance preferences into physical quadrupedal robots. Within minutes, Text2Robot can use text-to-3D models to provide strong initializations of diverse morphologies. Within a day, our geometric processing algorithms and body-control co-optimization produce a walking robot by explicitly considering real-world electronics and manufacturability. Text2Robot enables rapid prototyping and opens new opportunities for robot design with generative models.
Physics-Informed Split Koopman Operators for Data-Efficient Soft Robotic Simulation
Venue: Conference ICRA 2025
Year: 2025

Koopman operator theory provides a powerful data-driven technique for modeling nonlinear dynamical systems in a linear framework, in comparison to computationally expensive and highly nonlinear physics-based simulations. However, Koopman operator-based models for soft robots are very high dimensional and require considerable amounts of data to properly resolve. Inspired by physics-informed techniques from machine learning, we present a novel physics-informed Koopman operator identification method that improves simulation accuracy for small dataset sizes. Through Strang splitting, the method takes advantage of both continuous and discrete Koopman operator approximation to obtain information both from trajectory and phase space data. The method is validated on a tendon-driven soft robotic arm, showing orders of magnitude improvement over standard methods in terms of the shape error. We envision this method can significantly reduce the data requirement of Koopman operators for systems with partially known physical models, and thus reduce the cost of obtaining data.
PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation
Venue: Conference ICRA 2025
Year: 2025

Tactile sensing is vital for human dexterous manipulation, however, it has not been widely used in robotics. Compact, low-cost sensing platforms can facilitate a change, but unlike their popular optical counterparts, they are difficult to deploy in high-fidelity tasks due to their low signal dimensionality and lack of a simulation model. To overcome these challenges, we introduce PseudoTouch which links high-dimensional structural information to low-dimensional sensor signals. It does so by learning a low-dimensional visual-tactile embedding, wherein we encode a depth patch from which we decode the tactile signal. We collect and train PseudoTouch on a dataset comprising aligned tactile and visual data pairs obtained through random touching of eight basic geometric shapes. We demonstrate the utility of our trained PseudoTouch model in two downstream tasks: object recognition and grasp stability prediction. In the object recognition task, we evaluate the learned embedding's performance on a set of five basic geometric shapes and five household objects. Using PseudoTouch, we achieve an object recognition accuracy 84% after just ten touches, surpassing a proprioception baseline. For the grasp stability task, we use ACRONYM labels to train and evaluate a grasp success predictor using PseudoTouch's predictions derived from virtual depth information. Our approach yields a 32% absolute improvement in accuracy compared to the baseline relying on partial point cloud data. We make the data, code, and trained models publicly available at https://pseudotouch.cs.uni-freiburg.de.
Flying Through Moving Gates without Full State Estimation
Venue: Conference ICRA 2025
Year: 2025

Autonomous drone racing requires powerful perception, planning, and control and has become a benchmark and test field for autonomous, agile flight. Existing work usually assumes static race tracks with known maps, which enables offline planning of time-optimal trajectories, performing localization to the gates to reduce the drift in visual-inertial odometry (VIO) for state estimation or training learning-based methods for the particular race track and operating environment. In contrast, many real-world tasks like disaster response or delivery need to be performed in unknown and dynamic environments. To make drone racing more robust against unseen environments and moving gates, we propose a control algorithm that operates without a race track map or VIO, relying solely on monocular measurements of the line of sight to the gates. For this purpose, we adopt the law of proportional navigation (PN) to accurately fly through the gates despite gate motions or wind. We formulate the PN-informed vision-based control problem for drone racing as a constrained optimization problem and derive a closed-form optimal solution. Through simulations and real-world experiments, we demonstrate that our algorithm can navigate through moving gates at high speeds while being robust to different gate movements, model errors, wind, and delays.
Composing Dextrous Grasping and In-Hand Manipulation via Scoring with a Reinforcement Learning Critic
Venue: Conference ICRA 2025
Year: 2025

In-hand manipulation and grasping are fundamental yet often separately addressed tasks in robotics. For deriving in-hand manipulation policies, reinforcement learning has recently shown great success. However, the derived controllers are not yet useful in real-world scenarios because they often require a human operator to place the objects in suitable initial (grasping) states. Finding stable grasps that also promote the desired in-hand manipulation goal is an open problem. In this work, we propose a method for bridging this gap by leveraging the critic network of a reinforcement learning agent trained for in-hand manipulation to score and select initial grasps. Our experiments show that this method significantly increases the success rate of in-hand manipulation without requiring additional training. We also present an implementation of a full grasp manipulation pipeline on a real-world system, enabling autonomous grasping and reorientation even of unwieldy objects.
M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning
Venue: Conference ICRA 2025
Year: 2025

Lifelong imitation learning for manipulation tasks poses significant challenges due to distribution shifts that occur in incremental learning steps. Existing methods often focus on unsupervised skill discovery to construct an ever-growing skill library or distillation from multiple policies, which can lead to scalability issues as diverse manipulation tasks are continually introduced and may fail to ensure a consistent latent space throughout the learning process, leading to catastrophic forgetting of previously learned skills. In this paper, we introduce M2Distill, a multi-modal distillation-based method for lifelong imitation learning focusing on preserving consistent latent space across vision, language, and action distributions throughout the learning process. By regulating the shifts in latent representations across different modalities from previous to current steps, and reducing discrepancies in Gaussian Mixture Model (GMM) policies between consecutive learning steps, we ensure that the learned policy retains its ability to perform previously learned tasks while seamlessly integrating new skills. Extensive evaluations on the LIBERO lifelong imitation learning benchmark suites, including LIBERO-OBJECT, LIBERO-GOAL, and LIBERO-SPATIAL, demonstrate that our method consistently outperforms prior state-of-the-art methods across all evaluated metrics.
MambaGlue: Fast and Robust Local Feature Matching with Mamba
Venue: Conference ICRA 2025
Year: 2025

In recent years, robust matching methods using deep learning-based approaches have been actively studied and improved in computer vision tasks. However, there remains a persistent demand for both robust and fast matching techniques. To address this, we propose a novel Mamba-based local feature matching approach, called MambaGlue, where Mamba is an emerging state-of-the-art architecture rapidly gaining recognition for its superior speed in both training and inference, and promising performance compared with Transformer architectures. In particular, we propose two modules: a) MambaAttention mixer to simultaneously and selectively understand the local and global context through the Mamba-based self-attention structure and b) deep confidence score regressor, which is a multi-layer perceptron (MLP)-based architecture that evaluates a score indicating how confidently matching predictions correspond to the ground-truth correspondences. Consequently, our MambaGlue achieves a balance between robustness and efficiency in real-world applications. As verified on various public datasets, we demonstrate that our MambaGlue yields a substantial performance improvement over baseline approaches while maintaining fast inference speed. Our code will be available on https://github.com/url-kaist/MambaGlue
Real-Time Sampling-based Online Planning for Drone Interception
Venue: Conference ICRA 2025
Year: 2025

This paper studies high-speed online planning in dynamic environments. The problem requires finding time-optimal trajectories that conform to system dynamics, meeting computational constraints for real-time adaptation, and accounting for uncertainty from environmental changes. To address these challenges, we propose a sampling-based online planning algorithm that leverages neural network inference to replace time-consuming nonlinear trajectory optimization, enabling rapid exploration of multiple trajectory options under uncertainty. The proposed method is applied to the drone interception problem, where a defense drone must intercept a target while avoiding collisions and handling imperfect target predictions. The algorithm efficiently generates trajectories toward multiple potential target drone positions in parallel. It then assesses trajectory reachability by comparing traversal times with the target drone's predicted arrival time, ultimately selecting the minimum-time reachable trajectory. Through extensive validation in both simulated and real-world environments, we demonstrate our method's capability for high-rate online planning and its adaptability to unpredictable movements in unstructured settings.
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills Using Large Language Models
Venue: Conference ICRA 2025
Year: 2025

Curriculum learning is a training mechanism in reinforcement learning (RL) that facilitates the achievement of complex policies by progressively increasing the task difficulty during training. However, designing effective curricula for a specific task often requires extensive domain knowledge and human intervention, which limits its applicability across various domains. Our core idea is that large language models (LLMs), with their extensive training on diverse language data and ability to encapsulate world knowledge, present significant potential for efficiently breaking down tasks and decomposing skills across various robotics environments. Additionally, the demonstrated success of LLMs in translating natural language into executable code for RL agents strengthens their role in generating task curricula. In this work, we propose CurricuLLM, which leverages the high-level planning and programming capabilities of LLMs for curriculum design, thereby enhancing the efficient learning of complex target tasks. CurricuLLM consists of: (Step 1) Generating sequence of subtasks that aid target task learning in natural language form, (Step 2) Translating natural language description of subtasks in executable task code, including the reward code and goal distribution code, and (Step 3) Evaluating trained policies based on trajectory rollout and subtask description. We evaluate CurricuLLM in various robotics simulation environments, ranging from manipulation, navigation, and locomotion, to show that CurricuLLM can aid learning complex robot control tasks. In addition, we validate humanoid locomotion policy learned through CurricuLLM in real-world. Project website is https://iconlab.negarmehr.com/CurricuLLM/
Limits of Specifiability for Sensor-Based Robotic Planning Tasks
Venue: Conference ICRA 2025
Year: 2025

There is now a large body of techniques, many based on formal methods, for describing and realizing complex robotics tasks, including those involving a variety of rich goals and time-extended behavior. This paper explores the limits of what sorts of tasks are specifiable, examining how the precise grounding of specifications, that is, whether the specification is given in terms of the robot's states, its actions and observations, its knowledge, or some other information,is crucial to whether a given task can be specified. While prior work included some description of particular choices for this grounding, our contribution treats this aspect as a first-class citizen: we introduce notation to deal with a large class of problems, and examine how the grounding affects what tasks can be posed. The results demonstrate that certain classes of tasks are specifiable under different combinations of groundings.
Reduced-Order Model-Based Gait Generation for Snake Robot Locomotion Using NMPC
Venue: Conference ICRA 2025
Year: 2025

This paper presents an optimization-based motion planning methodology for snake robots operating in constrained environments. By using a reduced-order model, the proposed approach simplifies the planning process, enabling the optimizer to autonomously generate gaits while constraining the robot's footprint within tight spaces. The method is validated through high-fidelity simulations that accurately model contact dynamics and the robot's motion. Key locomotion strategies are identified and further demonstrated through hardware experiments, including successful navigation through narrow corridors.
Digital Twins Meet the Koopman Operator: Data-Driven Learning for Robust Autonomy
Venue: Conference ICRA 2025
Year: 2025

Contrary to on-road autonomous navigation, off-road autonomy is complicated by various factors ranging from sensing challenges to terrain variability. In such a milieu, data-driven approaches have been commonly employed to capture intricate vehicle-environment interactions effectively. However, the success of data-driven methods depends crucially on the quality and quantity of data, which can be compromised by large variability in off-road environments. To address these concerns, we present a novel methodology to recreate the exact vehicle and its target operating conditions digitally for domain-specific data generation. This enables us to effectively model off-road vehicle dynamics from simulation data using the Koopman operator theory, and employ the obtained models for local motion planning and optimal vehicle control. The capabilities of the proposed methodology are demonstrated through an autonomous navigation problem of a 1:5 scale vehicle, where a terrain-informed planner is employed for global mission planning. Results indicate a substantial improvement in off-road navigation performance with the proposed algorithm (5.84x) and underscore the efficacy of digital twinning in terms of improving the sample efficiency (3.2x) and reducing the sim2real gap (5.2%).
Multi-Agent Obstacle Avoidance Using Velocity Obstacles and Control Barrier Functions
Venue: Conference ICRA 2025
Year: 2025

Velocity Obstacles (VO) methods form a paradigm for collision avoidance strategies among moving obstacles and agents. While VO methods perform well in simple multi-agent environments, they don't guarantee safety and can show overly conservative behavior in common situations. In this paper, we propose to combine a VO-strategy for guidance with a CBF-approach for safety, which overcomes the overly conservative behavior of VOs and formally guarantees safety. We validate our method in a baseline comparison study, using 2nd order integrator and car-like dynamics. Results support that our method outperforms the baselines w.r.t. path smoothness, collision avoidance, and success rates.
The Spinning Blimp: Design and Control of a Novel Minimalist Aerial Vehicle Leveraging Rotational Dynamics and Locomotion
Venue: Conference ICRA 2025
Year: 2025

This paper presents the Spinning Blimp, a novel lighter-than-air (LTA) aerial vehicle designed for low-energy stable flight. Utilizing an oblate spheroid helium balloon for buoyancy, the vehicle achieves minimal energy consumption while maintaining prolonged airborne states. The unique and low-cost design employs a passively arranged wing coupled with a propeller to induce a spinning behavior, providing inherent pendulum-like stabilization. We propose a control strategy that takes advantage of the continuous revolving nature of the spinning blimp to control translational motion. The cost-effectiveness of the vehicle makes it highly suitable for a variety of applications, such as patrolling, localization, air and turbulence monitoring, and domestic surveillance. Experimental evaluations affirm the design's efficacy and underscore its potential as a versatile and economically viable solution for aerial applications.
Estimating Commonsense Scene Composition on Belief Scene Graphs
Venue: Conference ICRA 2025
Year: 2025

This work establishes the concept of commonsense scene composition, with a focus on extending Belief Scene Graphs by estimating the spatial distribution of unseen objects. Specifically, the commonsense scene composition capability refers to the understanding of the spatial relationships among related objects in the scene, which in this article is modeled as a joint probability distribution for all possible locations of the semantic object class. The proposed framework includes two variants of a Correlation Information (CECI) model for learning probability distributions: (i) a baseline approach based on a Graph Convolutional Network, and (ii) a neuro-symbolic extension that integrates a spatial ontology based on Large Language Models (LLMs). Furthermore, this article provides a detailed description of the dataset generation process for such tasks. Finally, the framework has been validated through multiple runs on simulated data, as well as in a real-world indoor environment, demonstrating its ability to spatially interpret scenes across different room types.
Reactive Collision Avoidance for Safe Agile Navigation
Venue: Conference ICRA 2025
Year: 2025

Reactive collision avoidance is essential for agile robots navigating complex and dynamic environments, enabling real-time obstacle response. However, this task is inherently challenging because it requires a tight integration of perception, planning, and control, which traditional methods often handle separately, resulting in compounded errors and delays. This paper introduces a novel approach that unifies these tasks into a single reactive framework using solely onboard sensing and computing. Our method combines nonlinear model predictive control with adaptive control barrier functions, directly linking perception-driven constraints to real-time planning and control. Constraints are determined by using a neural network to refine noisy RGB-D data, enhancing depth accuracy, and selecting points with the minimum time-to-collision to prioritize the most immediate threats. To maintain a balance between safety and agility, a heuristic dynamically adjusts the optimization process, preventing overconstraints in real time. Extensive experiments with an agile quadrotor demonstrate effective collision avoidance across diverse indoor and outdoor environments, without requiring environment-specific tuning or explicit mapping.
A Map-Free Deep Learning-Based Framework for Gate-to-Gate Monocular Visual Navigation Aboard Miniaturized Aerial Vehicles
Venue: Conference ICRA 2025
Year: 2025

Palm-sized autonomous nano-drones, i.e., sub-50g in weight, recently entered the drone racing scenario, where they are tasked to avoid obstacles and navigate as fast as possible through gates. However, in contrast with their bigger counterparts, i.e., kg-scale drones, nano-drones expose three orders of magnitude less onboard memory and compute power, demanding more efficient and lightweight vision-based pipelines to win the race. This work presents a map-free vision-based (using only a monocular camera) autonomous nano-drone that combines a real-time deep learning gate detection front-end with a classic yet elegant and effective visual servoing control back-end, only relying on onboard resources. Starting from two state-of-the-art tiny deep learning models, we adapt them for our specific task, and after a mixed simulator-real-world training, we integrate and deploy them aboard our nano-drone. Our best-performing pipeline costs of only 24M multiply-accumulate operations per frame, resulting in a closed-loop control performance of 30 Hz, while achieving a gate detection root mean square error of 1.4 pixels, on our ~20k real-world image dataset. In-field experiments highlight the capability of our nano-drone to successfully navigate through 15 gates in 4 min, never crashing and covering a total travel distance of ~100m, with a peak flight speed of 1.9 m/s. Finally, to stress the generalization capability of our system, we also test it in a never-seen-before environment, where it navigates through gates for more than 4 min.
Obstacle-Avoidant Leader Following with a Quadruped Robot
Venue: Conference ICRA 2025
Year: 2025

Personal mobile robotic assistants are expected to find wide applications in industry and healthcare. For example, people with limited mobility can benefit from robots helping with daily tasks, or construction workers can have robots perform precision monitoring tasks on-site. However, manually steering a robot while in motion requires significant concentration from the operator, especially in tight or crowded spaces. This reduces walking speed, and the constant need for vigilance increases fatigue and, thus, the risk of accidents. This work presents a virtual leash with which a robot can naturally follow an operator. We use a sensor fusion based on a custom-built RF transponder, RGB cameras, and a LiDAR. In addition, we customize a local avoidance planner for legged platforms, which enables us to navigate dynamic and narrow environments. We successfully validate on the ANYmal platform the robustness and performance of our entire pipeline in real-world experiments.