OpenRaiser/Intern-Atlas huggingface dataset:
Synthesizing Grasps and Regrasps for Complex Manipulation Tasks
Venue: Conference ICRA 2025
Year: 2025

In complex manipulation tasks, e.g., manipulation by pivoting, the motion of the object being manipulated has to satisfy path constraints that can change during the motion. Therefore, a single grasp may not be sufficient for the entire path, and the object may need to be regrasped. Additionally, geometric data for objects from a sensor are usually available in the form of point clouds. The problem of computing grasps and regrasps from point-cloud representation of objects for complex manipulation tasks is a key problem in endowing robots with manipulation capabilities beyond pick-and-place. In this paper, we formalize the problem of grasping/regrasping for complex manipulation tasks with objects represented by (partial) point clouds and present an algorithm to solve it. We represent a complex manipulation task as a sequence of constant screw motions. Using a manipulation plan skeleton as a sequence of constant screw motions, we use a grasp metric to find graspable regions on the object for every constant screw segment. The overlap of the graspable regions for contiguous screws are then used to determine when and how many times the object needs to be regrasped. We present experimental results on point cloud data collected from RGB-D sensors to illustrate our approach.
A Hierarchical Graph-Based Terrain-Aware Autonomous Navigation Approach for Complementary Multimodal Ground-Aerial Exploration
Venue: Conference ICRA 2025
Year: 2025

Autonomous navigation in unknown environments is a fundamental challenge in robotics, particularly in coordinating ground and aerial robots to maximize exploration efficiency. This paper presents a novel approach that utilizes a hierarchical graph to represent the environment, encoding both geometric and semantic traversability. The framework enables the robots to compute a shared confidence metric, which helps the ground robot assess terrain and determine when deploying the aerial robot will extend exploration. The robot's confidence in traversing a path is based on factors such as predicted volumetric gain, path traversability, and collision risk. A hierarchy of graphs is used to maintain an efficient representation of traversability and frontier information through multi-resolution maps. Evaluated in a real subterranean exploration scenario, the approach allows the ground robot to autonomously identify zones that are no longer traversable but suitable for aerial deployment. By leveraging this hierarchical structure, the ground robot can selectively share graph information on confidence-assessed frontier targets from parts of the scene, enabling the aerial robot to navigate beyond obstacles and continue exploration.
A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards
Venue: Conference ICRA 2025
Year: 2025

Task specification for robotic manipulation in open-world environments is challenging, requiring flexible and adaptive objectives that align with human intentions and can evolve through iterative feedback. We introduce Iterative Keypoint Reward (IKER), a visually grounded, Python-based reward function that serves as a dynamic task specification. Our framework leverages VLMs to generate and refine these reward functions for multi-step manipulation tasks. Given RGB-D observations and free-form language instructions, we sample keypoints in the scene and generate a reward function conditioned on these keypoints. IKER operates on the spatial relationships between keypoints, leveraging commonsense priors about the desired behaviors, and enabling precise SE(3) control. We reconstruct real-world scenes in simulation and use the generated rewards to train reinforcement learning (RL) policies, which are then deployed into the real world-forming a real-to-sim-to-real loop. Our approach demonstrates notable capabilities across diverse scenarios, including both prehensile and non-prehensile tasks, showcasing multi-step task execution, spontaneous error recovery, and on-the-fly strategy adjustments. The results highlight IKER's effectiveness in enabling robots to perform multi-step tasks in dynamic environments through iterative reward shaping.
Bootstrapping Object-Level Planning with Large Language Models
Venue: Conference ICRA 2025
Year: 2025

We introduce a new method that extracts knowledge from a large language model (LLM) to produce object-level plans, which describe high-level changes to object state, and uses them to bootstrap task and motion planning (TAMP). Existing work uses LLMs to directly output task plans or generate goals in representations like PDDL. However, these methods fall short because they rely on the LLM to do the actual planning or output a hard-to-satisfy goal. Our approach instead extracts knowledge from an LLM in the form of plan schemas as an object-level representation called functional object-oriented networks (FOON), from which we automatically generate PDDL subgoals. Our method markedly outperforms alternative planning strategies in completing several pick-and-place tasks in simulation.
Data-Efficient Learning from Human Interventions for Mobile Robots
Venue: Conference ICRA 2025
Year: 2025

Mobile robots are essential in applications such as autonomous delivery and hospitality services. Applying learning-based methods to address mobile robot tasks has gained popularity due to its robustness and generalizability. Traditional methods such as Imitation Learning (IL) and Reinforcement Learning (RL) offer adaptability but require large datasets, carefully crafted reward functions, and face sim-to-real gaps, making them challenging for efficient and safe real-world deployment. We propose an online human-in-the-loop learning method PVP4Real that combines IL and RL to address these issues. PVP4Real enables efficient real-time policy learning from online human intervention and demonstration, without reward or any pretraining, significantly improving data efficiency and training safety. We validate our method by training two different robots -- a legged quadruped, and a wheeled delivery robot -- in two mobile robot tasks, one of which even uses raw RGBD image as observation. The training finishes within 15 minutes. Our experiments show the promising future of human-in-the-loop learning in addressing the data efficiency issue in real-world robotic tasks. More information is available at: https://metadriverse.github.io/pvp4real/
Towards Latency-Aware 3D Streaming Perception for Autonomous Driving
Venue: Conference ICRA 2025
Year: 2025

Although existing 3D perception algorithms have demonstrated significant improvements in performance, their deployment on edge devices continues to encounter critical challenges due to substantial runtime latency. We propose a new benchmark tailored for online evaluation by considering runtime latency. Based on the benchmark, we build a Latency-Aware 3D Streaming Perception (LASP) framework that addresses the latency issue through two primary components: 1) latency-aware history integration, which extends query propagation into a continuous process, ensuring the integration of historical feature regardless of varying latency; 2) latency-aware predictive detection, a module that compensates the detection results with the predicted trajectory and the posterior accessed latency. By incorporating the latency-aware mechanism, our method shows generalization across various latency levels, achieving an online performance that closely aligns with 80\% of its offline evaluation on the Jetson AGX Orin without any acceleration techniques.
Efficient Coordination and Synchronization of Multi-Robot Systems Under Recurring Linear Temporal Logic
Venue: Conference ICRA 2025
Year: 2025

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.
TCAFF: Temporal Consistency for Robot Frame Alignment
Venue: Conference ICRA 2025
Year: 2025

In the field of collaborative robotics, the ability to communicate spatial information like planned trajectories and shared environment information is crucial. When no global position information is available (e.g., indoor or GPS-denied environments), agents must align their coordinate frames before shared spatial information can be properly expressed and interpreted. Coordinate frame alignment is particularly difficult when robots have no initial alignment and are affected by odometry drift. To this end, we develop a novel multiple hypothesis algorithm, called TCAFF, for aligning the coordinate frames of neighboring robots. TCAFF considers potential alignments from associating sparse open-set object maps and leverages temporal consistency to determine an initial alignment and correct for drift, all without any initial knowledge of neighboring robot poses. We demonstrate TCAFF being used for frame alignment in a collaborative object tracking application on a team of four robots tracking six pedestrians and show that TCAFF enables robots to achieve a tracking accuracy similar to that of a system with ground truth localization. The code and hardware dataset are available at https://github.com/mit-acl/tcaff.
A Helping (Human) Hand in Kinematic Structure Estimation
Venue: Conference ICRA 2025
Year: 2025

Visual uncertainties such as occlusions, lack of texture, and noise present significant challenges in obtaining accurate kinematic models for safe robotic manipulation. We introduce a probabilistic real-time approach that leverages the human hand as a prior to mitigate these uncertainties. By tracking the constrained motion of the human hand during manipulation and explicitly modeling uncertainties in visual observations, our method reliably estimates an object's kinematic model online. We validate our approach on a novel dataset featuring challenging objects that are occluded during manipulation and offer limited articulations for perception. The results demonstrate that by incorporating an appropriate prior and explicitly accounting for uncertainties, our method produces accurate estimates, outperforming two recent baselines by 195% and 140%, respectively. Furthermore, we demonstrate that our approach's estimates are precise enough to allow a robot to manipulate even small objects safely.
Collision-Aware Traversability Analysis for Autonomous Vehicles in the Context of Agricultural Robotics
Venue: Conference ICRA 2025
Year: 2025

In this paper, we introduce a novel method for safe navigation in agricultural robotics. As global environmental challenges intensify, robotics offers a powerful solution to reduce chemical usage while meeting the increasing demands for food production. However, significant challenges remain in ensuring the autonomy and resilience of robots operating in unstructured agricultural environments. Obstacles such as crops and tall grass, which are deformable, must be identified as safely traversable, compared to rigid obstacles. To address this, we propose a new traversability analysis method based on a 3D spectral map reconstructed using a LIDAR and a multispectral camera. This approach enables the robot to distinguish between safe and unsafe collisions with deformable obstacles. We perform a comprehensive evaluation of multispectral metrics for vegetation detection and incorporate these metrics into an augmented environmental map. Utilizing this map, we compute a physics-based traversability metric that accounts for the robot's weight and size, ensuring safe navigation over deformable obstacles.
Kineto-Dynamical Planning and Accurate Execution of Minimum-Time Maneuvers on Three-Dimensional Circuits
Venue: Conference ICRA 2025
Year: 2025

Online planning and execution of minimum-time maneuvers on three-dimensional (3D) circuits is an open challenge in autonomous vehicle racing. In this paper, we present an artificial race driver (ARD) to learn the vehicle dynamics, plan and execute minimum-time maneuvers on a 3D track. ARD integrates a novel kineto-dynamical (KD) vehicle model for trajectory planning with economic nonlinear model predictive control (E-NMPC). We use a high-fidelity vehicle simulator (VS) to compare the closed-loop ARD results with a minimum-lap-time optimal control problem (MLT-VS), solved offline with the same VS. Our ARD sets lap times close to the MLT-VS, and the new KD model outperforms a literature benchmark. Finally, we study the vehicle trajectories, to assess the re-planning capabilities of ARD under execution errors. A video with the main results is available as supplementary material.
A Control Scheme for Collaborative Object Transportation between a Human and a Quadruped Robot Using the MIGHTY Suction Cup
Venue: Conference ICRA 2025
Year: 2025

In this work, a control scheme for human-robot collaborative object transportation is proposed, considering a quadruped robot equipped with the MIGHTY suction cup that serves both as a gripper for holding the object and a force/torque sensor. The proposed control scheme is based on the notion of admittance control, and incorporates a variable damping term aiming towards increasing the controllability of the human and, at the same time, decreasing her/his effort. Furthermore, to ensure that the object is not detached from the suction cup during the collaboration, an additional control signal is proposed, which is based on a barrier artificial potential. The proposed control scheme is proven to be passive and its performance is demonstrated through experimental evaluations conducted using the Unitree Go1 robot equipped with the MIGHTY suction cup.
TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals
Venue: Conference ICRA 2025
Year: 2025

Visual navigation in robotics traditionally relies on globally-consistent 3D maps or learned controllers, which can be computationally expensive and difficult to generalize across diverse environments. In this work, we present a novel RGB-only, object-level topometric navigation pipeline that enables zero-shot, long-horizon robot navigation without requiring 3D maps or pre-trained controllers. Our approach integrates global topological path planning with local metric trajectory control, allowing the robot to navigate towards object-level sub-goals while avoiding obstacles. We address key limitations of previous methods by continuously predicting local trajectory using monocular depth and traversability estimation, and incorporating an auto-switching mechanism that falls back to a baseline controller when necessary. The system operates using foundational models, ensuring open-set applicability without the need for domain-specific fine-tuning. We demonstrate the effectiveness of our method in both simulated environments and real-world tests, highlighting its robustness and deployability. Our approach outperforms existing state-of-the-art methods, offering a more adaptable and effective solution for visual navigation in open-set environments. The source code is made publicly available: https://github.com/podgorki/TANGO.
Whole-Body End-Effector Pose Tracking
Venue: Conference ICRA 2025
Year: 2025

Combining manipulation with the mobility of legged robots is essential for a wide range of robotic applications. However, integrating an arm with a mobile base significantly increases the system's complexity, making precise end-effector control challenging. Existing model-based approaches are often constrained by their modeling assumptions, leading to limited robustness. Meanwhile, recent Reinforcement Learning (RL) implementations restrict the arm's workspace to be in front of the robot or track only the position to obtain decent tracking accuracy. In this work, we address these limitations by introducing a whole-body RL formulation for end-effector pose tracking in a large workspace on rough, unstructured terrains. Our proposed method involves a terrain-aware sampling strategy for the robot's initial configuration and end-effector pose commands, as well as a game-based curriculum to extend the robot's operating range. We validate our approach on the ANYmal quadrupedal robot with a six DoF robotic arm. Through our experiments, we show that the learned controller achieves precise command tracking over a large workspace and adapts across varying terrains such as stairs and slopes. On deployment, it achieves a pose-tracking error of 2.64 cm and 3.64 degrees, outperforming existing competitive baselines.
Self-Mixing Laser Interferometry for Robotic Tactile Sensing
Venue: Conference ICRA 2025
Year: 2025

Self-mixing interferometry (SMI) has been lauded for its sensitivity in detecting microvibrations, while requiring no physical contact with its target. In robotics, microvibrations have traditionally been interpreted as a marker for object slip, and recently as a salient indicator of extrinsic contact. We present the first-ever robotic fingertip making use of SMI for slip and extrinsic contact sensing. The design is validated through measurement of controlled vibration sources, both before and after encasing the readout circuit in its fingertip package. Then, the SMI fingertip is compared to acoustic sensing through four experiments. The results are distilled into a technology decision map. SMI was found to be more sensitive to subtle slip events and significantly more resilient against ambient noise. We conclude that the integration of SMI in robotic fingertips offers a new, promising branch of tactile sensing in robotics. Design and data files are available at https://github.com/RemkoPr/icra2025-SMI-tactile-sensing.
Communication-Aware Iterative Map Compression for Online Path-Planning
Venue: Conference ICRA 2025
Year: 2025

This paper addresses the problem of optimizing communicated information among heterogeneous, resource-aware robot teams to facilitate their navigation. In such operations, a mobile robot compresses its local map to assist another robot in reaching a target within an uncharted environment. The primary challenge lies in ensuring that the map compression step balances network load while transmitting only the most essential information for effective navigation. We propose a communication framework that sequentially selects the optimal map compression in a task-driven, communication-aware manner. It introduces a decoder capable of iterative map estimation, handling noise through Kalman filter techniques. The computational speed of our decoder allows for a larger compression template set compared to previous methods, and enables applications in more challenging environments. Specifically, our simulations demonstrate a remarkable 98% reduction in communicated information, compared to a framework that transmits the raw data, on a large Mars inclination map and an Earth map, all while maintaining similar planning costs. Furthermore, our method significantly reduces computational time compared to the state-of-the-art approach.
From Simple to Complex Skills: The Case of In-Hand Object Reorientation
Venue: Conference ICRA 2025
Year: 2025

Learning policies in simulation and transferring them to the real world has become a promising approach in dexterous manipulation. However, bridging the sim-to-real gap for each new task requires substantial human effort, such as careful reward engineering, hyperparameter tuning, and system identification. In this work, we present a system that leverages low-level skills to address these challenges for more complex tasks. Specifically, we introduce a hierarchical policy for in-hand object reorientation based on previously acquired rotation skills. This hierarchical policy learns to select which low-level skill to execute based on feedback from both the environment and the low-level skill policies themselves. Compared to learning from scratch, the hierarchical policy is more robust to out-of-distribution changes and transfers easily from simulation to real-world environments. Additionally, we propose a generalizable object pose estimator that uses proprioceptive information, low-level skill predictions, and control errors as inputs to estimate the object pose over time. We demonstrate that our system can reorient objects, including symmetrical and textureless ones, to a desired pose.
Weathergs: 3D Scene Reconstruction in Adverse Weather Conditions Via Gaussian Splatting
Venue: Conference ICRA 2025
Year: 2025

3D Gaussian Splatting (3DGS) has gained significant attention for 3D scene reconstruction, but still suffers from complex outdoor environments, especially under adverse weather. This is because 3DGS treats the artifacts caused by adverse weather as part of the scene and will directly reconstruct them, largely reducing the clarity of the reconstructed scene. To address this challenge, we propose WeatherGS, a 3DGS-based framework for reconstructing clear scenes from multi-view images under different weather conditions. Specifically, we explicitly categorize the multi-weather artifacts into the dense particles and lens occlusions that have very different characters, in which the former are caused by snowflakes and raindrops in the air, and the latter are raised by the precipitation on the camera lens. In light of this, we propose a dense-to-sparse preprocess strategy, which sequentially removes the dense particles by an Atmospheric Effect Filter (AEF) and then extracts the relatively sparse occlusion masks with a Lens Effect Detector (LED). Finally, we train a set of 3D Gaussians by the processed images and generated masks for excluding occluded areas, and accurately recover the underlying clear scene by Gaussian splatting. We conduct a diverse and challenging benchmark to facilitate the evaluation of 3D reconstruction under complex weather scenarios. Extensive experiments on this benchmark demonstrate that our WeatherGS consistently produces high-quality, clean scenes across various weather scenarios, outperforming existing state-of-the-art methods. See project page:https://jumponthemoon.github.io/weather-gs.
AF-RLIO: Adaptive Fusion of Radar-LiDAR-Inertial Information for Robust Odometry in Challenging Environments
Venue: Conference ICRA 2025
Year: 2025

In robotic navigation, maintaining precise pose estimation and navigation in complex and dynamic environments is crucial. However, environmental challenges such as smoke, tunnels, and adverse weather can significantly degrade the performance of single-sensor systems like LiDAR or GPS, compromising the overall stability and safety of autonomous robots. To address these challenges, we propose AF-RLIO: an adaptive fusion approach that integrates 4D millimeter-wave radar, LiDAR, inertial measurement unit (IMU), and GPS to leverage the complementary strengths of these sensors for robust odometry estimation in complex environments. Our method consists of three key modules. Firstly, the pre-processing module utilizes radar data to assist LiDAR in removing dynamic points and determining when environmental conditions are degraded for LiDAR. Secondly, the dynamic-aware multimodal odometry selects appropriate point cloud data for scan-to-map matching and tightly couples it with the IMU using the Iterative Error State Kalman Filter. Lastly, the factor graph optimization module balances weights between odometry and GPS data, constructing a pose graph for optimization. The proposed approach has been evaluated on datasets and tested in real-world robotic environments, demonstrating its effectiveness and advantages over existing methods in challenging conditions such as smoke and tunnels.