Robotics
Scalable Coverage Trajectory Synthesis on GPUs as Statistical Inference
Max M. Sun, Jueun Kwon and Todd Murphey
Coverage motion planning is essential to a wide range of robotic tasks. Unlike conventional motion planning problems, which reason over temporal sequences of states, coverage motion planning requires
reasoning over the spatial distribution of entire trajectories, making standard motion planning methods limited in computational efficiency and less amenable to modern parallelization frameworks. In this work, we formulate the coverage motion planning problem as a statistical inference problem from the perspective of flow matching, a generative modeling technique that has gained significant
attention in recent years. The proposed formulation unifies commonly used statistical discrepancy measures, such as Kullback-Leibler divergence and Sinkhorn divergence, with a standard linear quadratic regulator problem. More importantly, it decouples the generation of trajectory gradients for coverage from the synthesis of control under nonlinear system dynamics, enabling significant acceleration through parallelization on modern computational architectures, particularly Graphics Processing Units (GPUs). This paper focuses on the advantages of this formulation in terms of scalability through parallelization, highlighting its computational benefits compared to conventional methods based on waypoint tracking.
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Robotics
Collaborative Representation Learning for Alignment of Tactile, Language, and Vision Modalities
Yiyun Zhou, Mingjing Xu, Jingwei Shi et al. (+2 more)
Tactile sensing offers rich and complementary information to
vision and
language, enabling
robots to perceive fine-grained object properties. However, existing tactile sensors lack standardization, leading to redundant features that hinder cross-sensor generalization. Moreover, existing methods fail to fully integrate the intermediate communication among tactile,
language, and
vision modalities. To address this, we propose TLV-CoRe, a CLIP-based Tactile-
Language-
Vision Collaborative Representation learning method. TLV-CoRe introduces a Sensor-Aware Modulator to unify tactile features across different sensors and employs tactile-irrelevant decoupled learning to disentangle irrelevant tactile features. Additionally, a Unified Bridging Adapter is introduced to enhance tri-modal interaction within the shared representation space. To fairly evaluate the effectiveness of tactile models, we further propose the RSS evaluation framework, focusing on Robustness, Synergy, and Stability across different methods. Experimental results demonstrate that TLV-CoRe significantly improves sensor-agnostic representation learning and cross-modal
alignment, offering a new direction for
multimodal tactile representation.
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All authors Yiyun Zhou, Mingjing Xu, Jingwei Shi, Quanjiang Li, Jingyuan Chen
Robotics
A Comparative Evaluation of Prominent Methods in Autonomous Vehicle Certification
Mustafa Erdem K{\i}rm{\i}z{\i}gül, Hasan Feyzi Doğruyol and Haluk Bayram
The "
Vision Zero" policy, introduced by the Swedish Parliament in 1997, aims to eliminate fatalities and serious injuries resulting from traffic accidents. To achieve this goal, the use of self-driving vehicles in traffic is envisioned and a roadmap for the certification of self-driving vehicles is aimed to be determined. However, it is still unclear how the basic safety requirements that autonomous vehicles must meet will be verified and certified, and which methods will be used. This paper focuses on the comparative evaluation of the prominent methods planned to be used in the certification process of autonomous vehicles. It examines the prominent methods used in the certification process, develops a pipeline for the certification process of autonomous vehicles, and determines the stages, actors, and areas where the addressed methods can be applied.
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Robotics
Rethinking Progression of Memory State in Robotic Manipulation: An Object-Centric Perspective
Nhat Chung, Taisei Hanyu, Toan Nguyen et al. (+9 more)
As
embodied agents operate in increasingly complex environments, the ability to perceive, track, and reason about individual object instances over time becomes essential, especially in tasks requiring sequenced interactions with visually similar objects. In these non-Markovian settings, key decision cues are often hidden in object-specific histories rather than the current scene. Without persistent
memory of prior interactions (what has been interacted with, where it has been, or how it has changed) visuomotor policies may fail, repeat past actions, or overlook completed ones. To surface this challenge, we introduce LIBERO-Mem, a non-Markovian task suite for stress-testing robotic manipulation under object-level partial observability. It combines short- and long-horizon object tracking with temporally sequenced subgoals, requiring
reasoning beyond the current frame. However,
vision-
language-action (VLA) models often struggle in such settings, with
token scaling quickly becoming intractable even for tasks spanning just a few hundred frames. We propose
Embodied-SlotSSM, a slot-centric VLA framework built for temporal scalability. It maintains spatio-temporally consistent slot identities and leverages them through two mechanisms: (1) slot-state-space modeling for reconstructing short-term history, and (2) a relational encoder to align the input
tokens with action decoding. Together, these components enable temporally grounded, context-aware action prediction. Experiments show
Embodied-SlotSSM's baseline performance on LIBERO-Mem and general tasks, offering a scalable solution for non-Markovian
reasoning in object-centric robotic policies.
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All authors Nhat Chung, Taisei Hanyu, Toan Nguyen, Huy Le, Frederick Bumgarner, Duy Minh Ho Nguyen, Khoa Vo, Kashu Yamazaki, Chase Rainwater, Tung Kieu, Anh Nguyen, Ngan Le
Robotics
SimTac: A Physics-Based Simulator for Vision-Based Tactile Sensing with Biomorphic Structures
Xuyang Zhang, Jiaqi Jiang, Zhuo Chen et al. (+5 more)
Tactile sensing in biological organisms is deeply intertwined with morphological form, such as human fingers, cat paws, and elephant trunks, which enables rich and adaptive interactions through a variety of geometrically complex structures. In contrast,
vision-based tactile sensors in robotics have been limited to simple planar geometries, with biomorphic designs remaining underexplored. To address this gap, we present SimTac, a physics-based simulation framework for the design and validation of biomorphic tactile sensors. SimTac consists of particle-based deformation modeling, light-field rendering for photorealistic tactile image generation, and a neural network for predicting mechanical responses, enabling accurate and efficient simulation across a wide range of geometries and materials. We demonstrate the versatility of SimTac by designing and validating physical sensor prototypes inspired by biological tactile structures and further demonstrate its effectiveness across multiple Sim2Real tactile tasks, including object classification, slip detection, and contact safety assessment. Our framework bridges the gap between bio-inspired design and practical realisation, expanding the design space of tactile sensors and paving the way for tactile sensing systems that integrate morphology and sensing to enable robust interaction in unstructured environments.
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All authors Xuyang Zhang, Jiaqi Jiang, Zhuo Chen, Yongqiang Zhao, Tianqi Yang, Daniel Fernandes Gomes, Jianan Wang, Shan Luo
Robotics · Cross-listed from eess.SP
RadAround: A Field-Expedient Direction Finder for Contested IoT Sensing & EM Situational Awareness
Owen A. Maute, Blake A. Roberts and Berker Peköz
This paper presents RadAround, a passive 2-D direction-finding system designed for
adversarial IoT sensing in contested environments. Using mechanically steered narrow-beam
antennas and field-deployable SCADA software, it generates high-resolution electromagnetic (EM) heatmaps using low-cost COTS or 3D-printed components. The microcontroller-deployable SCADA coordinates
antenna positioning and SDR sampling in real time for resilient, on-site operation. Its modular design enables rapid adaptation for applications such as EMC testing in disaster-response deployments, battlefield spectrum monitoring, electronic intrusion detection, and tactical EM situational awareness (EMSA). Experiments show RadAround detecting computing machinery through walls, assessing utilization, and pinpointing EM interference (EMI) leakage sources from Faraday enclosures.
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Robotics
Simulating an Autonomous System in CARLA using ROS 2
Joseph Abdo, Aditya Shibu, Moaiz Saeed et al. (+3 more)
Autonomous racing offers a rigorous setting to stress test perception, planning, and control under high speed and uncertainty. This paper proposes an approach to design and evaluate a software stack for an autonomous race car in CARLA: Car Learning to Act simulator, targeting competitive driving performance in the Formula Student UK Driverless (FS-AI) 2025 competition. By utilizing a 360{\deg} light detection and ranging (
LiDAR), stereo camera, global
navigation satellite system (GNSS), and inertial measurement unit (IMU) sensor via ROS 2 (
Robot Operating System), the system reliably detects the cones marking the track boundaries at distances of up to 35 m. Optimized trajectories are computed considering vehicle dynamics and simulated environmental factors such as visibility and lighting to navigate the track efficiently. The complete autonomous stack is implemented in ROS 2 and validated extensively in CARLA on a dedicated vehicle (ADS-DV) before being ported to the actual hardware, which includes the Jetson AGX Orin 64GB, ZED2i Stereo Camera, Robosense Helios 16P
LiDAR, and CHCNAV Inertial
Navigation System (INS).
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All authors Joseph Abdo, Aditya Shibu, Moaiz Saeed, Abdul Maajid Aga, Apsara Sivaprazad, Mohamed Al-Musleh
Robotics · Cross-listed from cs.CV
6D Strawberry Pose Estimation: Real-time and Edge AI Solutions Using Purely Synthetic Training Data
Saptarshi Neil Sinha, Julius Kühn, Mika Silvan Goschke et al. (+1 more)
Automated and selective harvesting of fruits has become an important area of research, particularly due to challenges such as high costs and a shortage of seasonal labor in advanced economies. This paper focuses on 6D pose estimation of strawberries using purely synthetic data generated through a procedural pipeline for photorealistic rendering. We employ the YOLOX-6D-Pose
algorithm, a single-shot approach that leverages the YOLOX backbone, known for its balance between speed and accuracy, and its support for edge inference. To address the lacking availability of training data, we introduce a robust and flexible pipeline for generating synthetic strawberry data from various 3D models via a procedural Blender pipeline, where we focus on enhancing the realism of the synthesized data in comparison to previous work to make it a valuable resource for training pose estimation
algorithms. Quantitative evaluations indicate that our models achieve comparable accuracy on both the NVIDIA RTX 3090 and Jetson Orin Nano across several ADD-S metrics, with the RTX 3090 demonstrating superior processing speed. However, the Jetson Orin Nano is particularly suited for resource-constrained environments, making it an excellent choice for deployment in agricultural robotics. Qualitative assessments further confirm the model's performance, demonstrating its capability to accurately infer the poses of ripe and partially ripe strawberries, while facing challenges in detecting unripe specimens. This suggests opportunities for future improvements, especially in enhancing detection capabilities for unripe strawberries (if desired) by exploring variations in color. Furthermore, the methodology presented could be adapted easily for other fruits such as apples, peaches, and plums, thereby expanding its applicability and impact in the field of agricultural automation.
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All authors Saptarshi Neil Sinha, Julius Kühn, Mika Silvan Goschke, Michael Weinmann
Robotics
Experiences from Benchmarking Vision-Language-Action Models for Robotic Manipulation
Yihao Zhang, Yuankai Qi and Xi Zheng
Foundation models applied in robotics, particularly
Vision–
Language–Action (VLA) models, hold great promise for achieving general-purpose manipulation. Yet, systematic real-world evaluations and cross-model comparisons remain scarce. This paper reports our empirical experiences from benchmarking four representative VLAs – ACT, OpenVLA–OFT, RDT-1B, and \boldmath{$\pi_0$} – across four manipulation tasks conducted in both simulation and on the ALOHA Mobile platform. We establish a standardized evaluation framework that measures performance along three key dimensions: (1) accuracy and efficiency (success rate and time-to-success), (2) adaptability across in-distribution, spatial out-of-distribution, and instance-plus-spatial out-of-distribution settings, and (3)
language instruction-following accuracy. Through this process, we observe that \boldmath{$\pi_0$} demonstrates superior adaptability in out-of-distribution scenarios, while ACT provides the highest stability in-distribution. Further analysis highlights differences in computational demands, data-scaling behavior, and recurring failure modes such as near-miss grasps, premature releases, and long-horizon state drift. These findings reveal practical trade-offs among VLA model architectures in balancing precision, generalization, and deployment cost, offering actionable insights for selecting and deploying VLAs in real-world robotic manipulation tasks.
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Robotics
Sashimi-Bot: Autonomous Tri-manual Advanced Manipulation and Cutting of Deformable Objects
Sverre Herland, Amit Parag, Elling Ruud Øye et al. (+9 more)
Advanced robotic manipulation of deformable, volumetric objects remains one of the greatest challenges due to their pliancy, frailness, variability, and uncertainties during interaction. Motivated by these challenges, this article introduces Sashimi-Bot, an autonomous multi-robotic system for advanced manipulation and cutting, specifically the preparation of sashimi. The objects that we manipulate, salmon loins, are natural in origin and vary in size and shape, they are limp and deformable with poorly characterized elastoplastic parameters, while also being slippery and hard to hold. The three
robots straighten the loin; grasp and hold the knife; cut with the knife in a slicing motion while cooperatively stabilizing the loin during cutting; and pick up the thin slices from the cutting board or knife blade. Our system combines deep reinforcement learning with in-hand tool shape manipulation, in-hand tool cutting, and feedback of visual and tactile information to achieve robustness to the variabilities inherent in this task. This work represents a milestone in robotic manipulation of deformable, volumetric objects that may inspire and enable a wide range of other real-world applications.
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All authors Sverre Herland, Amit Parag, Elling Ruud Øye, Fangyi Zhang, Fouad Makiyeh, Aleksander Lillienskiold, Abhaya Pal Singh, Edward H. Adelson, Francois Chaumette, Alexandre Krupa, Peter Corke, Ekrem Misimi
Robotics
Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum
Chenhao Liu, Leyun Jiang, Ningyuan Tian et al. (+4 more)
Humanoid robots have demonstrated strong capabilities for interacting with static scenes across locomotion and manipulation, yet dynamic real-world interactions remain challenging. As a step toward fast-moving object interactions, we present an RL training pipeline that yields a unified whole-body controller for
humanoid badminton, coordinating footwork and striking without motion priors or expert demonstrations. In badminton, locomotion and striking are tightly coupled, making the final hitting objective difficult to optimize directly due to sparse rewards and conflicting gradients. We address this with an annealed curriculum that first stabilizes learning through auxiliary locomotion objectives, then progressively removes them to focus optimization on the final hitting objective. For deployment, we use an Extended Kalman Filter (EKF) to estimate and predict shuttlecock trajectories for target striking, and also develop a prediction-free variant that removes the EKF and explicit prediction. We validate the framework in simulation and on hardware. In simulation, two
robots sustain a rally of 21 consecutive hits. In real-world tests with both machine-fed shuttles and human-
robot rallies, the
robot achieves outgoing shuttle speeds up to 19.1 m/s. Moreover, the prediction-free variant attains comparable performance to the EKF-based policy. Overall, our approach enables dynamic yet precise goal striking in
humanoid badminton and suggests a path toward more dynamics-critical whole-body interaction tasks.
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All authors Chenhao Liu, Leyun Jiang, Ningyuan Tian, Yibo Wang, Kairan Yao, Jinchen Fu, Xiaoyu Ren
Robotics · Cross-listed from cs.CV
Phys-Liquid: A Physics-Informed Dataset for Estimating 3D Geometry and Volume of Transparent Deformable Liquids
Ke Ma, Yizhou Fang, Jean-Baptiste Weibel et al. (+5 more)
Estimating the
geometric and volumetric properties of transparent deformable
liquids is challenging due to optical complexities and dynamic surface deformations induced by container movements. Autonomous
robots performing precise
liquid manipulation tasks, such as dispensing, aspiration, and mixing, must handle containers in ways that inevitably induce these deformations, complicating accurate
liquid state assessment. Current
datasets lack comprehensive physics-informed simulation data representing realistic
liquid behaviors under diverse dynamic scenarios. To bridge this gap, we introduce Phys-
Liquid, a physics-informed
dataset comprising 97,200 simulation images and corresponding 3D meshes, capturing
liquid dynamics across multiple laboratory scenes, lighting conditions,
liquid colors, and container rotations. To validate the realism and effectiveness of Phys-
Liquid, we propose a four-stage reconstruction and estimation pipeline involving
liquid segmentation, multi-view mask generation, 3D mesh reconstruction, and real-world scaling. Experimental results demonstrate improved accuracy and consistency in reconstructing
liquid geometry and volume, outperforming existing
benchmarks. The
dataset and associated validation methods facilitate future advancements in transparent
liquid perception tasks. The
dataset and code are available at https://dualtransparency.github.io/Phys-
Liquid/.
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All authors Ke Ma, Yizhou Fang, Jean-Baptiste Weibel, Shuai Tan, Xinggang Wang, Yang Xiao, Yi Fang, Tian Xia
Robotics
AdaptPNP: Integrating Prehensile and Non-Prehensile Skills for Adaptive Robotic Manipulation
Jinxuan Zhu, Chenrui Tie, Xinyi Cao et al. (+8 more)
Non-prehensile (NP) manipulation, in which
robots alter object states without forming stable grasps (for example, pushing, poking, or sliding), significantly broadens robotic manipulation capabilities when grasping is infeasible or insufficient. However, enabling a unified framework that generalizes across different tasks, objects, and environments while seamlessly integrating non-prehensile and prehensile (P) actions remains challenging:
robots must determine when to invoke NP skills, select the appropriate primitive for each context, and compose P and NP strategies into robust, multi-step plans. We introduce ApaptPNP, a
vision-
language model (VLM)-empowered task and motion planning framework that systematically selects and combines P and NP skills to accomplish diverse manipulation objectives. Our approach leverages a VLM to interpret visual scene observations and textual task descriptions, generating a high-level plan skeleton that prescribes the sequence and coordination of P and NP actions. A digital-twin based object-centric intermediate layer predicts desired object poses, enabling proactive mental rehearsal of manipulation sequences. Finally, a control module synthesizes low-level
robot commands, with continuous execution feedback enabling online task plan refinement and adaptive replanning through the VLM. We evaluate ApaptPNP across representative P&NP hybrid manipulation tasks in both simulation and real-world environments. These results underscore the potential of hybrid P&NP manipulation as a crucial step toward general-purpose, human-level robotic manipulation capabilities. Project Website: https://adaptpnp.github.io/
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All authors Jinxuan Zhu, Chenrui Tie, Xinyi Cao, Yuran Wang, Jingxiang Guo, Zixuan Chen, Haonan Chen, Junting Chen, Yangyu Xiao, Ruihai Wu, Lin Shao
Robotics · Cross-listed from cs.AI
Autonomous Vehicle Path Planning by Searching With Differentiable Simulation
Asen Nachkov, Jan-Nico Zaech, Danda Pani Paudel et al. (+2 more)
Planning allows an
agent to safely refine its actions before executing them in the real world. In autonomous driving, this is crucial to avoid collisions and navigate in complex, dense traffic scenarios. One way to plan is to search for the best action sequence. However, this is challenging when all necessary components - policy, next-state predictor, and critic - have to be learned. Here we propose Differentiable Simulation for Search (DSS), a framework that leverages the differentiable simulator Waymax as both a next state predictor and a critic. It relies on the simulator's hardcoded dynamics, making state predictions highly accurate, while utilizing the simulator's differentiability to effectively search across action sequences. Our DSS
agent optimizes its actions using gradient descent over imagined future trajectories. We show experimentally that DSS - the combination of planning gradients and
stochastic search - significantly improves tracking and path planning accuracy compared to sequence prediction, imitation learning, model-free RL, and other planning methods.
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All authors Asen Nachkov, Jan-Nico Zaech, Danda Pani Paudel, Xi Wang, Luc Van Gool
Robotics
Miniature Testbed for Validating Multi-Agent Cooperative Autonomous Driving
Hyunchul Bae, Eunjae Lee, Jehyeop Han et al. (+5 more)
Cooperative autonomous driving, which extends vehicle autonomy by enabling real-time collaboration between vehicles and smart roadside infrastructure, remains a challenging yet essential problem. However, none of the existing testbeds employ smart infrastructure equipped with sensing, edge computing, and communication capabilities. To address this gap, we design and implement a 1:15-scale miniature testbed, CIVAT, for validating cooperative autonomous driving, consisting of a scaled urban map, autonomous vehicles with onboard sensors, and smart infrastructure. The proposed testbed integrates V2V and V2I communication with the publish-subscribe pattern through a shared Wi-Fi and ROS2 framework, enabling information exchange between vehicles and infrastructure to realize cooperative driving functionality. As a case study, we validate the system through infrastructure-based perception and intersection management experiments.
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All authors Hyunchul Bae, Eunjae Lee, Jehyeop Han, Minhee Kang, Jaehyeon Kim, Junggeun Seo, Minkyun Noh, Heejin Ahn
Robotics
Efficient Image-Goal Navigation with Representative Latent World Model
Zhiwei Zhang, Hui Zhang, Kaihong Huang et al. (+2 more)
World models enable
robots to conduct counterfactual
reasoning in physical environments by predicting future world states. While conventional approaches often prioritize pixel-level reconstruction of future scenes, such detailed rendering is computationally intensive and unnecessary for planning tasks like
navigation. We therefore propose that prediction and planning can be efficiently performed directly within a latent space of high-level semantic representations. To realize this, we introduce the Representative Latent space
Navigation World Model (ReL-NWM). Rather than relying on reconstructionoriented latent
embeddings, our method leverages a pre-trained representation encoder, DINOv3, and incorporates specialized mechanisms to effectively integrate action signals and historical context within this representation space. By operating entirely in the latent domain, our model bypasses expensive explicit reconstruction and achieves highly efficient
navigation planning. Experiments show state-of-the-art trajectory prediction and image-goal
navigation performance on multiple
benchmarks. Additionally, we demonstrate real-world applicability by deploying the system on a Unitree G1
humanoid robot, confirming its efficiency and robustness in practical
navigation scenarios.
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All authors Zhiwei Zhang, Hui Zhang, Kaihong Huang, Chenghao Shi, Huimin Lu
Robotics
Dynamic Reconfiguration of Robotic Swarms: Coordination and Control for Precise Shape Formation
Prab Prasertying, Paulo Garcia and Warisa Sritriratanarak
Coordination of movement and configuration in robotic swarms is a challenging endeavor. Deciding when and where each individual
robot must move is a computationally complex problem. The challenge is further exacerbated by difficulties inherent to physical systems, such as measurement error and control dynamics. Thus, how to best determine the optimal path for each
robot, when moving from one configuration to another, and how to best perform such determination and effect corresponding motion remains an open problem. In this paper, we show an
algorithm for such coordination of robotic swarms. Our methods allow seamless transition from one configuration to another, leveraging
geometric formulations that are mapped to the physical domain through appropriate control, localization, and mapping techniques. This paves the way for novel applications of robotic swarms by enabling more sophisticated distributed behaviors.
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Robotics
Dexterous Manipulation Transfer via Progressive Kinematic-Dynamic Alignment
Wenbin Bai, Qiyu Chen, Xiangbo Lin et al. (+4 more)
The inherent difficulty and limited scalability of collecting manipulation data using multi-fingered
robot hand hardware platforms have resulted in severe data scarcity, impeding research on data-driven dexterous manipulation policy learning. To address this challenge, we present a hand-agnostic manipulation transfer system. It efficiently converts human hand manipulation sequences from demonstration
videos into high-quality dexterous manipulation trajectories without requirements of massive training data. To tackle the multi-dimensional disparities between human hands and dexterous hands, as well as the challenges posed by high-degree-of-freedom coordinated control of dexterous hands, we design a progressive transfer framework: first, we establish primary control signals for dexterous hands based on kinematic matching; subsequently, we train residual policies with action space rescaling and thumb-guided initialization to dynamically optimize contact interactions under unified rewards; finally, we compute wrist control trajectories with the objective of preserving operational semantics. Using only human hand manipulation
videos, our system automatically configures system parameters for different tasks, balancing kinematic matching and dynamic optimization across dexterous hands, object categories, and tasks. Extensive experimental results demonstrate that our framework can automatically generate smooth and semantically correct dexterous hand manipulation that faithfully reproduces human intentions, achieving high efficiency and strong generalizability with an average transfer success rate of 73%, providing an easily implementable and scalable method for collecting
robot dexterous manipulation data.
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All authors Wenbin Bai, Qiyu Chen, Xiangbo Lin, Jianwen Li, Quancheng Li, Hejiang Pan, Yi Sun
Robotics
Terradynamics and design of tip-extending robotic anchors
Deniz Kerimoglu, Nicholas D. Naclerio, Sean Chu et al. (+5 more)
Most engineered pilings require substantially more force to be driven into the ground than they can resist during extraction. This requires relatively heavy equipment for insertion, which is problematic for anchoring in hard-to-access sites, including in extraterrestrial locations. In contrast, for tree roots, the external reaction force required to extract is much greater than required to insert–little more than the weight of the seed initiates insertion. This is partly due to the mechanism by which roots insert into the ground: tip extension. Proof-of-concept robotic prototypes have shown the benefits of using this mechanism, but a rigorous understanding of the underlying granular mechanics and how they inform the design of a robotic anchor is lacking. Here, we study the terradynamics of tip-extending anchors compared to traditional piling-like intruders, develop a set of design insights, and apply these to create a deployable robotic anchor. Specifically, we identify that to increase an anchor's ratio of extraction force to insertion force, it should: (i) extend beyond a critical depth; (ii) include hair-like protrusions; (iii) extend near-vertically, and (iv) incorporate multiple smaller anchors rather than a single large anchor. Synthesizing these insights, we developed a lightweight, soft robotic, root-inspired anchoring device that inserts into the ground with a reaction force less than its weight. We demonstrate that the 300 g device can deploy a series of temperature sensors 45 cm deep into loose Martian regolith simulant while anchoring with an average of 120 N, resulting in an anchoring-to-weight ratio of 40:1.
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All authors Deniz Kerimoglu, Nicholas D. Naclerio, Sean Chu, Andrew Krohn, Vineet Kupunaram, Alexander Schepelmann, Daniel I. Goldman, Elliot W. Hawkes
Robotics
Collaborative Multi-Robot Non-Prehensile Manipulation via Flow-Matching Co-Generation
Yorai Shaoul, Zhe Chen, Mohamed Naveed Gul Mohamed et al. (+3 more)
Coordinating a team of
robots to reposition multiple objects in cluttered environments requires
reasoning jointly about where
robots should establish contact, how to manipulate objects once contact is made, and how to navigate safely and efficiently at scale. Prior approaches typically fall into two extremes – either learning the entire task or relying on privileged information and hand-designed planners – both of which struggle to handle diverse objects in long-horizon tasks. To address these challenges, we present a unified framework for collaborative multi-
robot, multi-object non-prehensile manipulation that integrates flow-matching co-generation with anonymous multi-
robot motion planning. Within this framework, a generative model co-generates contact formations and manipulation trajectories from visual observations, while a novel motion planner conveys
robots at scale. Crucially, the same planner also supports coordination at the object level, assigning manipulated objects to larger target structures and thereby unifying
robot- and object-level
reasoning within a single algorithmic framework. Experiments in challenging simulated environments demonstrate that our approach outperforms baselines in both motion planning and manipulation tasks, highlighting the benefits of generative co-design and integrated planning for scaling collaborative manipulation to complex multi-
agent, multi-object settings. Visit gco-paper.github.io for code and demonstrations.
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All authors Yorai Shaoul, Zhe Chen, Mohamed Naveed Gul Mohamed, Federico Pecora, Maxim Likhachev, Jiaoyang Li
Robotics
WetExplorer: Automating Wetland Greenhouse-Gas Surveys with an Autonomous Mobile Robot
Jose Vasquez and Xuping Zhang
Quantifying greenhouse-gases (GHG) in wetlands is critical for
climate modeling and restoration assessment, yet manual sampling is labor-intensive, and time demanding. We present WetExplorer, an autonomous tracked
robot that automates the full GHG-sampling workflow. The
robot system integrates low-ground-pressure locomotion, centimeter-accurate lift placement, dual-RTK sensor fusion, obstacle avoidance planning, and deep-learning perception in a containerized ROS2 stack. Outdoor trials verified that the sensor-fusion stack maintains a mean localization error of 1.71 cm, the
vision module estimates object pose with 7 mm translational and 3{\deg} rotational accuracy, while indoor trials demonstrated that the full motion-planning pipeline positions the sampling chamber within a global tolerance of 70 mm while avoiding obstacles, all without human intervention. By eliminating the manual bottleneck, WetExplorer enables high-frequency, multi-site GHG measurements and opens the door for dense, long-duration
datasets in saturated wetland terrain.
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