OpenRaiser/Intern-Atlas huggingface dataset:
Improving Grip Stability Using Passive Compliant Microspine Arrays for Soft Robots in Unstructured Terrain
Venue: Conference ICRA 2025
Year: 2025

Microspine grippers are small spines commonly found on insect legs that reinforce surface interaction by engaging with asperities to increase shear force and traction. An array of such microspines, when integrated into the limbs or undercarriage of a robot, can provide the ability to maneuver uneven terrains, traverse inclines, and even climb walls. Conformability and adaptability of soft robots makes them ideal candidates for these applications involving traversal of complex, unstructured terrains. However, there remains a real-life realization gap for soft locomotors pertaining to their transition from controlled lab environment to the field by improving grip stability through effective integration of microspines. We propose a passive, compliant microspine stacked array design to enhance the locomotion capabilities of mobile soft robots, in our case, ones that are motor tendon actuated. We offer a standardized microspine array integration method with effective soft-compliant stiffness integration, and reduced complexity resulting from a single actuator passively controlling them. The presented design utilizes a two-row, stacked microspine array configuration that offers additional gripping capabilities on extremely steep/irregular surfaces from the top row while not hindering the effectiveness of the more frequently active bottom row. We explore different configurations of the microspine array to account for changing surface topologies and enable independent, adaptable gripping of asperities per microspine. Field test experiments are conducted on various rough surfaces including concrete, brick, compact sand, and tree roots with three robots consisting of a baseline without microspines compared against two robots with different combinations of microspine arrays. Tracking results indicate that the inclusion of microspine arrays increases planar displacement on average by 15 and 8 times.
Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm
Venue: Conference ICRA 2025
Year: 2025

While legged robots have achieved significant advancements in recent years, ensuring the robustness of their controllers on unstructured terrains remains challenging. It requires generating diverse and challenging unstructured terrains to test the robot and discover its vulnerabilities. This topic remains underexplored in the literature. This paper presents a Quality-Diversity framework to generate diverse and challenging terrains that uncover weaknesses in legged robot controllers. Our method, applied to both simulated bipedal and quadruped robots, produces an archive of terrains optimized to challenge the controller in different ways. Quantitative and qualitative analyses show that the generated archive effectively contains terrains that the robots struggled to traverse, presenting different failure modes. Interesting results were observed, including failure cases that were not necessarily expected. Experiments show that the generated terrains can also be used to improve RL-based controllers.
Reduced-Order Model Guided Contact-Implicit Model Predictive Control for Humanoid Locomotion
Venue: Conference ICRA 2025
Year: 2025

Humanoid robots have great potential for real-world applications due to their ability to operate in environments built for humans, but their deployment is hindered by the challenge of controlling their underlying high-dimensional nonlinear hybrid dynamics. While reduced-order models like the Hybrid Linear Inverted Pendulum (HLIP) are simple and computationally efficient, they lose whole-body expressiveness. Meanwhile, recent advances in Contact-Implicit Model Predictive Control (CI-MPC) enable robots to plan through multiple hybrid contact modes, but remain vulnerable to local minima and require significant tuning. We propose a control framework that combines the strengths of HLIP and CI-MPC. The reduced-order model generates a nominal gait, while CI-MPC manages the whole-body dynamics and modifies the contact schedule as needed. We demonstrate the effectiveness of this approach in simulation with a novel 24 degree-of-freedom humanoid robot: Achilles. Our proposed framework achieves rough terrain walking, disturbance recovery, robustness under model and state uncertainty, and allows the robot to interact with obstacles in the environment, all while running online in real-time at 50 Hz.
Multimodal Behaviour Trees for Robotic Laboratory Task Automation
Venue: Conference ICRA 2025
Year: 2025

Laboratory robotics offer the capability to conduct experiments with a high degree of precision and reproducibility, with the potential to transform scientific research. Trivial and repeatable tasks; e.g., sample transportation for analysis and vial capping are well-suited for robots; if done successfully and reliably, chemists could contribute their efforts towards more critical research activities. Currently, robots can perform these tasks faster than chemists, but how reliable are they? Improper capping could result in human exposure to toxic chemicals which could be fatal. To ensure that robots perform these tasks as accurately as humans, sensory feedback is required to assess the progress of task execution. To address this, we propose a novel methodology based on behaviour trees with multimodal perception. Along with automating robotic tasks, this methodology also verifies the successful execution of the task, a fundamental requirement in safety-critical environments. The experimental evaluation was conducted on two lab tasks: sample vial capping and laboratory rack insertion. The results show high success rate, i.e., 88% for capping and 92% for insertion, along with strong error detection capabilities. This ultimately proves the robustness and reliability of our approach and that using multimodal behaviour trees should pave the way towards the next generation of robotic chemists.
NeuGrasp: Generalizable Neural Surface Reconstruction with Background Priors for Material-Agnostic Object Grasp Detection
Venue: Conference ICRA 2025
Year: 2025

Robotic grasping in scenes with transparent and specular objects presents great challenges for methods relying on accurate depth information. In this paper, we introduce NeuGrasp, a neural surface reconstruction method that leverages background priors for material-agnostic grasp detection. NeuGrasp integrates transformers and global prior volumes to aggregate multi-view features with spatial encoding, enabling robust surface reconstruction in narrow and sparse viewing conditions. By focusing on foreground objects through residual feature enhancement and refining spatial perception with an occupancy-prior volume, NeuGrasp excels in handling objects with transparent and specular surfaces. Extensive experiments in both simulated and real-world scenarios show that NeuGrasp outperforms state-of-the-art methods in grasping while maintaining comparable reconstruction quality. More details are available at https://neugrasp.github.io/.
Universal Online Temporal Calibration for Optimization-Based Visual-Inertial Navigation Systems
Venue: Conference ICRA 2025
Year: 2025

6-Degree of Freedom (6DoF) motion estimation with a combination of visual and inertial sensors is a growing area with numerous real-world applications. However, precise calibration of the time offset between these two sensor types is a prerequisite for accurate and robust tracking. To address this, we propose a universal online temporal calibration strategy for optimization-based visual-inertial navigation systems. Technically, we incorporate the time offset td as a state parameter in the optimization residual model to align the IMU state to the corresponding image timestamp using td, angular velocity and translational velocity. This allows the temporal misalignment td to be optimized alongside other tracking states during the process. As our method only modifies the structure of the residual model, it can be applied to various optimization-based frameworks with different tracking frontends. We evaluate our calibration method with both EuRoC and simulation data and extensive experiments demonstrate that our approach provides more accurate time offset estimation and faster convergence, particularly in the presence of noisy sensor data.
FLEX: A Framework for Learning Robot-Agnostic Force-Based Skills Involving Sustained Contact Object Manipulation
Venue: Conference ICRA 2025
Year: 2025

Learning to manipulate objects efficiently, particularly those involving sustained contact (e.g., pushing, sliding) and articulated parts (e.g., drawers, doors), presents significant challenges. Traditional methods, such as robot-centric reinforcement learning (RL), imitation learning, and hybrid techniques, require massive training and often struggle to generalize across different objects and robot platforms. We propose a novel framework for learning object-centric manipulation policies in force space, decoupling the robot from the object. By directly applying forces to selected regions of the object, our method simplifies the action space, reduces unnecessary exploration, and decreases simulation overhead. This approach, trained in simulation on a small set of representative objects, captures object dynamics -- such as joint configurations -- allowing policies to generalize effectively to new, unseen objects. Decoupling these policies from robot-specific dynamics enables direct transfer to different robotic platforms (e.g., Kinova, Panda, UR5) without retraining. Our evaluations demonstrate that the method significantly outperforms baselines, achieving over an order of magnitude improvement in training efficiency compared to other state-of-the-art methods. Additionally, operating in force space enhances policy transferability across diverse robot platforms and object types. We further showcase the applicability of our method in a real-world robotic setting. For supplementary materials and videos, please visit: https://tufts-ai-robotics-group.github.io/FLEX/
CubeDN: Real-Time Drone Detection in 3D Space from Dual mmWave Radar Cubes
Venue: Conference ICRA 2025
Year: 2025

As drone use has become more widespread, there is a critical need to ensure safety and security. A key element of this is robust and accurate drone detection and localization. While cameras and other optical sensors like LiDAR are commonly used for object detection, their performance degrades under adverse lighting and environmental conditions. Therefore, this has generated interest in finding more reliable alternatives, such as millimeter-wave (mmWave) radar. Recent research on mmWave radar object detection has predominantly focused on 2D detection of road users. Although these systems demonstrate excellent performance for 2D problems, they lack the sensing capability to measure elevation, which is essential for 3D drone detection. To address this gap, we propose CubeDN, a single-stage end-to-end radar object detection network specifically designed for flying drones. CubeDN overcomes challenges such as poor elevation resolution by utilizing a dual radar configuration and a novel deep learning pipeline. It simultaneously detects, localizes, and classifies drones of two sizes, achieving decimeter-level tracking accuracy at closer ranges with overall $95\%$ average precision (AP) and $85\%$ average recall (AR). Furthermore, CubeDN completes data processing and inference at 10Hz, making it highly suitable for practical applications.
Foundation Models for Rapid Autonomy Validation
Venue: Conference ICRA 2025
Year: 2025

We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it could encounter, including rare events, to provide a strong case for safety and show there is no edge-case pathological behavior. Autonomous vehicle companies rely on potentially millions of miles driven in realistic simulation to expose the driving stack to enough miles to estimate rates and severity of collisions. To address scalability and coverage, we propose the use of a behavior foundation model, specifically a masked autoencoder (MAE), trained to reconstruct driving scenarios. We leverage the foundation model in two complementary ways: we (i) use the learned embedding space to group qualitatively similar scenarios together and (ii) fine-tune the model to label scenario difficulty based on the likelihood of a collision upon simulation. We use the difficulty scoring as importance weighting for the groups of scenarios. The result is an approach which can more rapidly estimate the rates and severity of collisions by prioritizing hard scenarios while ensuring exposure to every kind of driving scenario.
Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing
Venue: Conference ICRA 2025
Year: 2025

Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their usefulness in demanding real-world applications such as search and rescue, construction, industrial automation, and room organization. This paper tackles the task of obstacle-aware, long-horizon pushing by multiple quadrupedal robots. We propose a hierarchical multi-agent reinforcement learning framework with three levels of control. The high-level controller integrates an RRT planner and a centralized adaptive policy to generate subgoals, while the mid-level controller uses a decentralized goal-conditioned policy to guide the robots toward these sub-goals. A pre-trained low-level locomotion policy executes the movement commands. We evaluate our method against several baselines in simulation, demonstrating significant improvements over baseline approaches, with 36.0% higher success rates and 24.5% reduction in completion time than the best baseline. Our framework successfully enables long-horizon, obstacle-aware manipulation tasks like Push-Cuboid and Push-T on Go1 robots in the real world.
Safe Multi-Agent Navigation Guided by Goal-Conditioned Safe Reinforcement Learning
Venue: Conference ICRA 2025
Year: 2025

Safe navigation is essential for autonomous systems operating in hazardous environments. Traditional planning methods excel at long-horizon tasks but rely on a predefined graph with fixed distance metrics. In contrast, safe Reinforcement Learning (RL) can learn complex behaviors without relying on manual heuristics but fails to solve long-horizon tasks, particularly in goal-conditioned and multi-agent scenarios. In this paper, we introduce a novel method that integrates the strengths of both planning and safe RL. Our method leverages goal-conditioned RL and safe RL to learn a goal-conditioned policy for navigation while concurrently estimating cumulative distance and safety levels using learned value functions via an automated self-training algorithm. By constructing a graph with states from the replay buffer, our method prunes unsafe edges and generates a waypoint-based plan that the agent follows until reaching its goal, effectively balancing faster and safer routes over extended distances. Utilizing this unified high-level graph and a shared low-level goal-conditioned safe RL policy, we extend this approach to address the multi-agent safe navigation problem. In particular, we leverage Conflict-Based Search (CBS) to create waypoint-based plans for multiple agents allowing for their safe navigation over extended horizons. This integration enhances the scalability of goal-conditioned safe RL in multi-agent scenarios, enabling efficient coordination among agents. Extensive benchmarking against state-of-the-art baselines demonstrates the effectiveness of our method in achieving distance goals safely for multiple agents in complex and hazardous environments. Our code and further details about or work is available at https://safe-visual-mapf-mers.csail.mit.edu/.
One Net to Rule Them All: Domain Randomization in Quadcopter Racing Across Different Platforms
Venue: Conference ICRA 2025
Year: 2025

In high-speed quadcopter racing, finding a single controller that works well across different platforms remains challenging. This work presents the first neural network controller for drone racing that generalizes across physically distinct quadcopters. We demonstrate that a single network, trained with domain randomization, can robustly control various types of quadcopters. The network relies solely on the current state to directly compute motor commands. The effectiveness of this generalized controller is validated through real-world tests on two substantially different crafts (3-inch and 5-inch race quadcopters). We further compare the performance of this generalized controller with controllers specifically trained for the 3-inch and 5-inch drone, using their identified model parameters with varying levels of domain randomization (0%, 10%, 20%, 30%). While the generalized controller shows slightly slower speeds compared to the fine-tuned models, it excels in adaptability across different platforms. Our results show that no randomization fails sim-to-real transfer while increasing randomization improves robustness but reduces speed. Despite this trade-off, our findings highlight the potential of domain randomization for generalizing controllers, paving the way for universal AI controllers that can adapt to any platform.
ROS2WASM: Bringing the Robot Operating System to the Web
Venue: Conference ICRA 2025
Year: 2025

The Robot Operating System (ROS) has become the de facto standard middleware in robotics, widely adopted across domains ranging from education to industrial applications. The RoboStack distribution, a conda-based packaging system for ROS, has extended ROS's accessibility by facilitating installation across all major operating systems and architectures, integrating seamlessly with scientific tools such as PyTorch and Open3D. This paper presents ROS2WASM, a novel integration of RoboStack with WebAssembly, enabling the execution of ROS 2 and its associated software directly within web browsers, without requiring local installations. ROS2WASM significantly enhances the reproducibility and shareability of research, lowers barriers to robotics education, and leverages WebAssembly's robust security framework to protect against malicious code. We detail our methodology for cross-compiling ROS 2 packages into WebAssembly, the development of a specialized middleware for ROS 2 communication within browsers, and the implementation of https://www.ros2wasm.dev, a web platform enabling users to interact with ROS 2 environments. Additionally, we extend support to the Robotics Toolbox for Python and adapt its Swift simulator for browser compatibility. Our work paves the way for unprecedented accessibility in robotics, offering scalable, secure, and reproducible environments that have the potential to transform educational and research paradigms.
Parallel-Constraint Model Predictive Control: Exploiting Parallel Computation for Improving Safety
Venue: Conference ICRA 2025
Year: 2025

Ensuring constraint satisfaction is a key requirement for safety-critical systems, which include most robotic platforms. For example, constraints can be used for modeling joint position/velocity/torque limits and collision avoidance. Constrained systems are often controlled using Model Predictive Control, because of its ability to naturally handle constraints, relying on numerical optimization. However, ensuring constraint satisfaction is challenging for nonlinear systems/constraints. A well-known tool to make controllers safe is the so-called control-invariant set (a.k.a. safe set). In our previous work, we have shown that safety can be improved by letting the safe-set constraint recede along the MPC horizon. In this paper, we push that idea further by exploiting parallel computation to improve safety. We solve several MPC problems at the same time, where each problem instantiates the safe-set constraint at a different time step along the horizon. Finally, the controller can select the best solution according to some user-defined criteria. We validated this idea through extensive simulations with a 3-joint robotic arm, showing that significant improvements can be achieved in terms of safety and performance, even using as little as 4 computational cores.
Suture Thread Modeling Using Control Barrier Functions for Autonomous Surgery
Venue: Conference ICRA 2025
Year: 2025

Automating surgical systems enhances precision and safety while reducing human involvement in high-risk environments. A major challenge in automating surgical procedures like suturing is accurately modeling the suture thread, a highly flexible and compliant component. Existing models either lack the accuracy needed for safety critical procedures or are too computationally intensive for real time execution. In this work, we introduce a novel approach for modeling suture thread dynamics using control barrier functions (CBFs), achieving both realism and computational efficiency. Thread like behavior, collision avoidance, stiffness, and damping are all modeled within a unified CBF and control Lyapunov function (CLF) framework. Our approach eliminates the need to calculate complex forces or solve differential equations, significantly reducing computational overhead while maintaining a realistic model suitable for both automation and virtual reality surgical training systems. The framework also allows visual cues to be provided based on the thread's interaction with the environment, enhancing user experience when performing suture or ligation tasks. The proposed model is tested on the MagnetoSuture system, a minimally invasive robotic surgical platform that uses magnetic fields to manipulate suture needles, offering a less invasive solution for surgical procedures.
UAV-Assisted Self-Supervised Terrain Awareness for Off-Road Navigation
Venue: Conference ICRA 2025
Year: 2025

Terrain awareness is an essential milestone to enable truly autonomous off-road navigation. Accurately predicting terrain characteristics allows optimizing a vehicle's path against potential hazards. Recent methods use deep neural networks to predict traversability-related terrain properties in a self-supervised manner, relying on proprioception as a training signal. However, onboard cameras are inherently limited by their point-of-view relative to the ground, suffering from occlusions and vanishing pixel density with distance. This paper introduces a novel approach for self-supervised terrain characterization using an aerial perspective from a hovering drone. We capture terrain-aligned images while sampling the environment with a ground vehicle, effectively training a simple predictor for vibrations, bumpiness, and energy consumption. Our dataset includes 2.8 km of off-road data collected in forest environment, comprising 13 484 ground-based images and 12 935 aerial images. Our findings show that drone imagery improves terrain property prediction by 21.37 % on the whole dataset and 37.35 % in high vegetation, compared to ground robot images. We conduct ablation studies to identify the main causes of these performance improvements. We also demonstrate the real-world applicability of our approach by scouting an unseen area with a drone, planning and executing an optimized path on the ground.