Robotics
Three Dimensional Hydrodynamic Flow-Based Collision Avoidance for UAV Formations Facing Emergent Dynamic Obstacles
Suguru Sato and Kamesh Subbarao
This paper presents a three-dimensional, hydrodynamics-inspired collision avoidance framework for uncrewed aerial vehicle (
UAV) formations operating in dynamic environments. When moving obstacles enter a
UAV's sensing region, they are modeled as three dimensional doublets or ellipsoids that generate local velocity fields, guiding nearby
UAVs to execute smooth, collision-free maneuvers without trajectory discontinuities or explicit trajectory replanning. This flow-based approach enables real-time operation and interpretable behavior by leveraging the nature of fluid flow around obstacles via the harmonic properties of Laplace's equation, inherently avoiding local minima common in traditional potential field methods. To establish and maintain coordination among the
UAVs, a Virtual Rigid Body (VRB) formation strategy is integrated, ensuring that formation geometry and trajectory tracking are preserved. Simulation results demonstrate the feasibility and scalability of the method for both individual and multi-
UAV scenarios with multiple formation geometries encountering moving obstacles. The proposed approach achieves safe, smooth, and computationally efficient avoidance maneuvers suitable for real-time and practical applications.
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Robotics · Cross-listed from cs.HC
A Hybrid Soft Haptic Display for Rendering Lump Stiffness in Remote Palpation
Pijuan Yu, Anzu Kawazoe, Alexis Urquhart et al. (+3 more)
Remote palpation enables noninvasive tissue examination in telemedicine, yet current tactile displays often lack the fidelity to convey both large-scale forces and fine spatial details. This study introduces a hybrid fingertip display comprising a rigid platform and a $4\times4$ soft pneumatic tactile display (4.93 mm displacement and 1.175 N per single pneumatic chamber) to render a hard lump beneath soft tissue. This study compares three rendering strategies: a Platform-Only baseline that renders the total interaction force; a Hybrid A (Position + Force Feedback) strategy that adds a dynamic, real-time soft spatial cue; and a Hybrid B (Position + Preloaded Stiffness Feedback) strategy that provides a constant, pre-calculated soft spatial cue. In a 12-participant lump detection study, both hybrid methods dramatically improved accuracy over the Platform-Only baseline (from 50% to over 95%). While the Hybrid B was highlighted qualitatively for realism, its event-based averaging is expected to increase interaction latency in real-time operation. This suggests a trade-off between perceived lump realism and real-time responsiveness, such that rendering choices that enhance realism may conflict with those that minimize latency.
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All authors Pijuan Yu, Anzu Kawazoe, Alexis Urquhart, Thomas K. Ferris, M. Cynthia Hipwell, Rebecca F. Friesen
Robotics
Optimal Thruster Configuration for 6-DOF Control of a Small Satellite
Suguru Sato, Jinaykumar Patel and Kamesh Subbarao
With the growing deployment of small satellites (such as CubeSats, Nanosats, Picosats, and Femtosats) in Low Earth Orbit (LEO) for targeted applications like imaging, communication, data storage, and rendezvous-docking mission, there is increasing
attention on orbit maintenance and attitude control. A common approach for active orbit control involves the use of multiple thrusters, which, when properly arranged, can also generate the required torque for attitude control. Starting from a 24-thruster configuration, this paper presents a set of thruster configurations (referred to as a viable configuration group) that enable full six degrees of freedom (6-DOF) control. Further, configuration group that requires minimum total thrust to achieve 6-DOF commands are found among the viable configuration group. One configuration from each of these groups is further evaluated for its attitude control performance through a representative rendezvous-docking mission, demonstrating that even with a reduced thruster count, sufficient maneuverability can be achieved.
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Robotics
RobotDesignGPT: Automated Robot Design Synthesis using Vision Language Models
Nitish Sontakke, K. Niranjan Kumar and Sehoon Ha
Robot design is a nontrivial process that involves careful consideration of multiple criteria, including user specifications, kinematic structures, and visual appearance. Therefore, the design process often relies heavily on domain expertise and significant human effort. The majority of current methods are rule-based, requiring the specification of a grammar or a set of primitive components and modules that can be composed to create a design. We propose a novel automated
robot design framework, RobotDesignGPT, that leverages the general knowledge and
reasoning capabilities of large pre-trained
vision-
language models to automate the
robot design synthesis process. Our framework synthesizes an initial
robot design from a simple user
prompt and a reference image. Our novel visual feedback approach allows us to greatly improve the design quality and reduce unnecessary manual feedback. We demonstrate that our framework can design visually appealing and kinematically valid
robots inspired by nature, ranging from legged animals to flying creatures. We justify the proposed framework by conducting an ablation study and a user study.
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Robotics · Cross-listed from cs.LG
Physics-Constrained Denoising Autoencoders for Data-Scarce Wildfire UAV Sensing
Abdelrahman Ramadan, Zahra Dorbeigi Namaghi, Emily Taylor et al. (+5 more)
Wildfire monitoring requires high-resolution atmospheric measurements, yet low-cost sensors on Unmanned Aerial Vehicles (
UAVs) exhibit baseline drift, cross-sensitivity, and response lag that corrupt concentration estimates. Traditional deep learning denoising approaches demand large
datasets impractical to obtain from limited
UAV flight campaigns. We present PC$^2$DAE, a physics-informed denoising autoencoder that addresses data scarcity by
embedding physical constraints directly into the network architecture. Non-negative concentration estimates are enforced via softplus activations and physically plausible temporal smoothing, ensuring outputs are physically admissible by construction rather than relying on loss function penalties. The architecture employs hierarchical decoder heads for Black Carbon, Gas, and CO$_2$ sensor families, with two variants: PC$^2$DAE-Lean (21k parameters) for edge deployment and PC$^2$DAE-Wide (204k parameters) for offline processing. We evaluate on 7,894 synchronized 1 Hz samples collected from
UAV flights during prescribed burns in Saskatchewan, Canada (approximately 2.2 hours of flight data), two orders of magnitude below typical deep learning requirements. PC$^2$DAE-Lean achieves 67.3% smoothness improvement and 90.7% high-frequency noise reduction with zero physics violations. Five baselines (LSTM-AE, U-Net,
Transformer, CBDAE, DeSpaWN) produce 15–23% negative outputs. The lean variant outperforms wide (+5.6% smoothness), suggesting reduced capacity with strong inductive bias prevents overfitting in data-scarce regimes. Training completes in under 65 seconds on consumer hardware.
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All authors Abdelrahman Ramadan, Zahra Dorbeigi Namaghi, Emily Taylor, Lucas Edwards, Xan Giuliani, David S. McLagan, Sidney Givigi, Melissa Greeff
Robotics · Cross-listed from cs.CV
UAV-Based Infrastructure Inspections: A Literature Review and Proposed Framework for AEC+FM
Amir Farzin Nikkhah, Dong Chen, Bradford Campbell et al. (+2 more)
Unmanned Aerial Vehicles (
UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) domain. By synthesizing insights from over 150 studies, this review paper highlights
UAV-based methodologies for data acquisition, photogrammetric modeling, defect detection, and decision-making support. Key innovations include path optimization, thermal integration, and advanced machine learning (ML) models such as YOLO and Faster R-CNN for anomaly detection.
UAVs have demonstrated value in structural health monitoring (SHM), disaster response, urban infrastructure management, energy efficiency evaluations, and cultural heritage preservation. Despite these advancements, challenges in real-time processing,
multimodal data fusion, and generalizability remain. A proposed workflow framework, informed by literature and a case study, integrates RGB imagery,
LiDAR, and thermal sensing with
transformer-based architectures to improve accuracy and reliability in detecting structural defects, thermal anomalies, and
geometric inconsistencies. The proposed framework ensures precise and actionable insights by fusing
multimodal data and dynamically adapting path planning for complex environments, presented as a comprehensive step-by-step guide to address these challenges effectively. This paper concludes with future research directions emphasizing lightweight AI models, adaptive flight planning, synthetic
datasets, and richer modality fusion to streamline modern infrastructure inspections.
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All authors Amir Farzin Nikkhah, Dong Chen, Bradford Campbell, Somayeh Asadi, Arsalan Heydarian
Robotics
Semantic-Geometric Task Representations for Bimanual Manipulation from Human Demonstrations to Robot Action Planning
Franziska Herbert, Vignesh Prasad, Han Liu et al. (+2 more)
Learning structured task representations from human demonstrations is essential for bimanual manipulation, where action ordering, object involvement, and interaction geometry vary significantly across executions. A key challenge lies in jointly capturing the discrete semantic task structure and the temporal
evolution of object-centric
geometric relations in a form that supports
reasoning over task progression. We introduce a semantic-
geometric graph-based task representation that jointly encodes object identities, inter-object semantic relations, and per-object motion histories, via a Message Passing Neural Network (MPNN) encoder and a
Transformer-based decoder. The encoder operates solely on the temporal scene graph, producing structured representations decoupled from action labels. The decoder then conditions on action-context to forecast future actions, associated objects, and object motions. This decoupling learns task-agnostic representations, enabling encoder reuse across embodiments through decoder-only finetuning on a small
robot dataset. Across eleven bimanual tasks from two
datasets, we find that the benefit of structured semantic–
geometric representations over simpler sequence-based models grows with task variability in action ordering and object involvement. At deployment, a planner couples the action and motion predictions with learned Probabilistic Movement Primitives, achieving full task success on two real-
robot bimanual tasks and outperforming graph ablations,
Transformer, decoder-only, and finetuned
vision-
language model baselines.
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All authors Franziska Herbert, Vignesh Prasad, Han Liu, Dorothea Koert, Georgia Chalvatzaki
Robotics · Cross-listed from eess.SY
Learning-Based Shrinking Disturbance-Invariant Tubes for State- and Input-Dependent Uncertainty
Abdelrahman Ramadan and Sidney Givigi
We develop a learning-based framework for constructing shrinking disturbance-invariant tubes under state- and input-dependent uncertainty, intended as a building block for tube Model Predictive Control (MPC), and certify safety via a lifted, isotone (order-preserving) fixed-point map. Gaussian Process (GP) posteriors become $(1-\alpha)$ credible ellipsoids, then polytopic outer sets for deterministic set operations. A two-time-scale scheme separates learning epochs, where these polytopes are frozen, from an inner, outside-in iteration that converges to a compact fixed point $Z^\star\!\subseteq\!\mathcal G$; its state projection is RPI for the plant. As data accumulate, disturbance polytopes tighten, and the associated tubes nest monotonically, resolving the circular dependence between the set to be verified and the disturbance model while preserving hard constraints. A double-integrator study illustrates shrinking tube cross-sections in data-rich regions while maintaining invariance.
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Robotics
The Great March 100: 100 Detail-oriented Tasks for Evaluating Embodied AI Agents
Ziyu Wang, Chenyuan Liu, Yushun Xiang et al. (+16 more)
Recently, with the rapid development of
robot learning and imitation learning, numerous
datasets and methods have emerged. However, these
datasets and their task designs often lack systematic consideration and principles. This raises important questions: Do the current
datasets and task designs truly advance the capabilities of robotic
agents? Do evaluations on a few common tasks accurately reflect the differentiated performance of various methods proposed by different teams and evaluated on different tasks? To address these issues, we introduce the Great March 100 (GM-100) as the first step towards a
robot learning Olympics. GM-100 consists of 100 carefully designed tasks that cover a wide range of interactions and long-tail behaviors, aiming to provide a diverse and challenging set of tasks to comprehensively evaluate the capabilities of robotic
agents and promote diversity and complexity in
robot dataset task designs. These tasks are developed through systematic analysis and expansion of existing task designs, combined with insights from human-object interaction primitives and object affordances. We collect a large amount of trajectory data on different robotic platforms and evaluate several baseline models. Experimental results demonstrate that the GM-100 tasks are 1) feasible to execute and 2) sufficiently challenging to effectively differentiate the performance of current VLA models. Our data and code are available at https://rhos.ai/research/gm-100.
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All authors Ziyu Wang, Chenyuan Liu, Yushun Xiang, Runhao Zhang, Qingbo Hao, Hongliang Lu, Houyu Chen, Zhizhong Feng, Kaiyue Zheng, Dehao Ye, Xianchao Zeng, Xinyu Zhou, Boran Wen, Jiaxin Li, Mingyu Zhang, Kecheng Zheng, Qian Zhu, Ran Cheng, Yong-Lu Li
Robotics
ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models
Linqing Zhong, Yi Liu, Yifei Wei et al. (+4 more)
Vision-
Language-Action models have emerged as essential generalist
robot policies for diverse manipulation tasks, conventionally relying on directly translating
multimodal inputs into actions via
Vision-
Language Model
embeddings. Recent advancements have introduced explicit intermediary
reasoning-such as sub-task prediction (
language) or goal image synthesis (
vision)-to guide action generation. However, these intermediate
reasoning are often indirect and inherently limited in their capacity to convey the full, granular information required for precise action execution. Instead, we posit that the most effective form of
reasoning is one that deliberates directly in the action space. We introduce Action Chain-of-Thought (ACoT), a paradigm where the
reasoning process itself is formulated as a structured sequence of coarse action intents that guide the final policy. In this paper, we propose ACoT-VLA, a novel architecture that materializes the ACoT paradigm. Specifically, we introduce two complementary components: an Explicit Action Reasoner (EAR) and Implicit Action Reasoner (IAR). The former proposes coarse reference trajectories as explicit action-level
reasoning steps, while the latter extracts latent action priors from internal representations of
multimodal input, co-forming an ACoT that conditions the downstream action head to enable grounded policy learning. Extensive experiments in real-world and simulation environments demonstrate the superiority of our proposed method. Code is available at: https://github.com/AgibotTech/ACoT-VLA.
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All authors Linqing Zhong, Yi Liu, Yifei Wei, Ziyu Xiong, Maoqing Yao, Si Liu, Guanghui Ren
Robotics
The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning
Henrik Hose, Paul Brunzema, Devdutt Subhasish et al. (+1 more)
The development of robust learning-based control
algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a significant barrier for many researchers. This paper introduces a comprehensive dynamics
dataset for the Mini Wheelbot, an open-source, quasi-symmetric balancing reaction wheel unicycle. The
dataset provides 1 kHz synchronized data encompassing all onboard sensor readings, state estimates, ground-truth poses from a motion capture system, and third-person
video logs. To ensure data diversity, we include experiments across multiple hardware instances and surfaces using various control paradigms, including pseudo-random binary excitation, nonlinear model predictive control, and reinforcement learning
agents. We include several example applications in dynamics model learning, state estimation, and time-series classification to illustrate common robotics
algorithms that can be benchmarked on our
dataset.
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All authors Henrik Hose, Paul Brunzema, Devdutt Subhasish, Sebastian Trimpe
Robotics
Distributed Control Barrier Functions for Safe Multi-Vehicle Navigation in Heterogeneous USV Fleets
Tyler Paine, Brendan Long, Jeremy Wenger et al. (+3 more)
Collision avoidance in heterogeneous fleets of uncrewed vessels is challenging because the decision-making processes and controllers often differ between platforms, and it is further complicated by the limitations on sharing trajectories and control values in real-time. This paper presents a pragmatic approach that addresses these issues by adding a control filter on each autonomous vehicle that assumes worst-case behavior from other contacts, including crewed vessels. This distributed safety control filter is developed using control barrier function (CBF) theory and the application is clearly described to ensure explainability of these safety-critical methods. This work compares the worst-case CBF approach with a Collision Regulations (COLREGS) behavior-based approach in simulated encounters. Real-world experiments with three different uncrewed vessels and a human operated vessel were performed to confirm the approach is effective across a range of platforms and is robust to uncooperative behavior from human operators. Results show that combining both CBF methods and COLREGS behaviors achieves the best safety and efficiency.
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All authors Tyler Paine, Brendan Long, Jeremy Wenger, Michael DeFilippo, James Usevitch, Michael Benjamin
Robotics
Skill-Aware Diffusion for Generalizable Robotic Manipulation
Aoshen Huang, Jiaming Chen, Jiyu Cheng et al. (+3 more)
Robust generalization in robotic manipulation is crucial for
robots to adapt flexibly to diverse environments. Existing methods usually improve generalization by scaling data and networks, but model tasks independently and overlook skill-level information. Observing that tasks within the same skill share similar motion patterns, we propose Skill-Aware
Diffusion (SADiff), which explicitly incorporates skill-level information to improve generalization. SADiff learns skill-specific representations through a skill-aware encoding module with learnable skill
tokens, and conditions a skill-constrained
diffusion model to generate object-centric motion flow. A skill-
retrieval transformation strategy further exploits skill-specific trajectory priors to refine the mapping from 2D motion flow to executable 3D actions. Furthermore, we introduce IsaacSkill, a high-fidelity
dataset containing fundamental robotic skills for comprehensive evaluation and sim-to-real transfer. Experiments in simulation and real-world settings show that SADiff achieves good performance and generalization across various manipulation tasks.
Dataset and
videos are available at https://sites.google.com/view/sa-diff.
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All authors Aoshen Huang, Jiaming Chen, Jiyu Cheng, Ran Song, Wei Pan, Wei Zhang
Robotics
VLAgents: A Policy Server for Efficient VLA Inference
Tobias Jülg, Khaled Gamal, Nisarga Nilavadi et al. (+5 more)
The rapid emergence of
Vision-
Language-Action models (VLAs) has a significant impact on robotics. However, their deployment remains complex due to the fragmented interfaces and the inherent communication latency in distributed setups. To address this, we introduce VLAgents, a modular policy server that abstracts VLA inferencing behind a unified Gymnasium-style protocol. Crucially, its communication layer transparently adapts to the context by supporting both zero-copy shared
memory for high-speed simulation and compressed streaming for remote hardware. In this work, we present the architecture of VLAgents and validate it by integrating seven policies – including OpenVLA and Pi Zero. In a
benchmark with both local and remote communication, we further demonstrate how it outperforms the default policy servers provided by OpenVLA, OpenPi, and LeRobot. VLAgents is available at https://github.com/RobotControlStack/vlagents
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All authors Tobias Jülg, Khaled Gamal, Nisarga Nilavadi, Pierre Krack, Seongjin Bien, Michael Krawez, Florian Walter, Wolfram Burgard
Robotics
Adaptive Monitoring of Stochastic Fire Front Processes via Information-seeking Predictive Control
Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou et al. (+1 more)
We consider the problem of adaptively monitoring a wildfire front using a mobile
agent (e.g., a drone), whose trajectory determines where sensor data is collected and thus influences the accuracy of fire propagation estimation. This is a challenging problem, as the
stochastic nature of wildfire
evolution requires the seamless integration of sensing, estimation, and control, often treated separately in existing methods. State-of-the-art methods either impose linear-Gaussian assumptions to establish optimality or rely on approximations and heuristics, often without providing explicit performance guarantees. To address these limitations, we formulate the fire front monitoring task as a
stochastic optimal control problem that integrates sensing, estimation, and control. We derive an optimal recursive
Bayesian estimator for a class of
stochastic nonlinear elliptical-growth fire front models. Subsequently, we transform the resulting nonlinear
stochastic control problem into a finite-horizon Markov decision process and design an information-seeking predictive control law obtained via a lower confidence bound-based adaptive search
algorithm with asymptotic convergence to the optimal policy.
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All authors Savvas Papaioannou, Panayiotis Kolios, Christos G. Panayiotou, Marios M. Polycarpou
Robotics
Learning Quadrupedal Locomotion for a Heavy Hydraulic Robot Using an Actuator Model
Minho Lee, Hyeonseok Kim, Jin Tak Kim et al. (+4 more)
The simulation-to-reality (sim-to-real) transfer of large-scale hydraulic
robots presents a significant challenge in robotics because of the inherent slow control response and complex fluid dynamics. The complex dynamics result from the multiple interconnected cylinder structure and the difference in fluid rates of the cylinders. These characteristics complicate detailed simulation for all joints, making it unsuitable for reinforcement learning (RL) applications. In this work, we propose an analytical actuator model driven by hydraulic dynamics to represent the complicated actuators. The model predicts joint torques for all 12 actuators in under 1 microsecond, allowing rapid processing in RL environments. We compare our model with neural network-based actuator models and demonstrate the advantages of our model in data-limited scenarios. The locomotion policy trained in RL with our model is deployed on a hydraulic quadruped
robot, which is over 300 kg. This work is the first demonstration of a successful transfer of stable and robust command-tracking locomotion with RL on a heavy hydraulic quadruped
robot, demonstrating advanced sim-to-real transferability.
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All authors Minho Lee, Hyeonseok Kim, Jin Tak Kim, Sangshin Park, Jeong Hyun Lee, Jungsan Cho, Jemin Hwangbo
Robotics
Visual Marker Search for Autonomous Drone Landing in Diverse Urban Environments
Jiaohong Yao, Linfeng Liang, Yao Deng et al. (+3 more)
Marker-based landing is widely used in drone delivery and return-to-base systems for its simplicity and reliability. However, most approaches assume idealized landing site visibility and sensor performance, limiting robustness in complex urban settings. We present a simulation-based evaluation suite on the AirSim platform with systematically varied urban layouts, lighting, and weather to replicate realistic operational diversity. Using onboard camera sensors (RGB for marker detection and depth for obstacle avoidance), we
benchmark two heuristic coverage patterns and a reinforcement learning-based
agent, analyzing how exploration strategy and scene complexity affect success rate, path efficiency, and robustness. Results underscore the need to evaluate marker-based autonomous landing under diverse, sensor-relevant conditions to guide the development of reliable aerial
navigation systems.
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All authors Jiaohong Yao, Linfeng Liang, Yao Deng, Xi Zheng, Richard Han, Yuankai Qi
Robotics
A3D: Adaptive Affordance Assembly with Dual-Arm Manipulation
Jiaqi Liang, Yue Chen, Qize Yu et al. (+4 more)
Furniture assembly is a crucial yet challenging task for
robots, requiring precise dual-arm coordination where one arm manipulates parts while the other provides collaborative support and stabilization. To accomplish this task more effectively,
robots need to actively adapt support strategies throughout the long-horizon assembly process, while also generalizing across diverse part geometries. We propose A3D, a framework which learns adaptive affordances to identify optimal support and stabilization locations on furniture parts. The method employs dense point-level
geometric representations to model part interaction patterns, enabling generalization across varied geometries. To handle evolving assembly states, we introduce an adaptive module that uses interaction feedback to dynamically adjust support strategies during assembly based on previous interactions. We establish a simulation environment featuring 50 diverse parts across 8 furniture types, designed for dual-arm collaboration evaluation. Experiments demonstrate that our framework generalizes effectively to diverse part geometries and furniture categories in both simulation and real-world settings.
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All authors Jiaqi Liang, Yue Chen, Qize Yu, Yan Shen, Haipeng Zhang, Hao Dong, Ruihai Wu
Robotics
EmboTeam: Grounding LLM Reasoning into Reactive Behavior Trees via PDDL for Embodied Multi-Robot Collaboration
Haishan Zeng, Mengna Wang and Peng Li
In
embodied artificial intelligence, enabling heterogeneous
robot teams to execute long-horizon tasks from high-level instructions remains a critical challenge. While large
language models (LLMs) show promise in instruction parsing and preliminary planning, they exhibit limitations in long-term
reasoning and dynamic multi-
robot coordination. We propose EmboTeam, a novel
embodied multi-
robot task planning framework that addresses these issues through a three-stage cascaded architecture: 1) It leverages an LLM to parse instructions and generate Planning Domain Definition
Language (PDDL) problem descriptions, thereby transforming commands into formal planning problems; 2) It combines the semantic
reasoning of LLMs with the search capabilities of a classical planner to produce optimized action sequences; 3) It compiles the resulting plan into behavior trees for reactive control. The framework supports dynamically sized heterogeneous
robot teams via a shared blackboard mechanism for communication and state synchronization. To validate our approach, we introduce the MACE-THOR
benchmark dataset, comprising 42 complex tasks across 8 distinct household layouts. Experiments show EmboTeam improves the task success rate from 12% to 55% and goal condition recall from 32% to 72% over the LaMMA-P baseline.
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Robotics · Cross-listed from cs.HC
Haptic Light-Emitting Diodes: Miniature, Luminous Tactile Actuators
Max Linnander and Yon Visell
We present Haptic Light-Emitting Diodes (HLEDs), luminous thermopneumatic actuators that directly convert pulsed light into mechanical forces and displacements. Each device packages a miniature surface-mount LED in a gas-filled cavity that contains a low-inertia graphite photoabsorber. The cavity is sealed by an elastic membrane, which functions as a working diaphragm. Brief optical pulses heat the photoabsorber, which heats the gas. The resulting rapid pressure increases generate forces and displacements at the working diaphragm. Millimeter-scale HLEDs produce forces exceeding 0.4 N and displacements of 0.9 mm at low voltages, with 5 to 100 ms response times, making them attractive as actuators providing tactile feedback in human-machine interfaces. Unusually, these actuators are also light-emitting, as a fraction of optical energy is transmitted through the membrane. These photomechanical actuators have many potential applications in tactile displays, human interface engineering, wearable computing, and other areas.
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Robotics
Crane Lowering Guidance Using a Attachable Camera Module for Driver Vision Support
HyoJae Kang, SunWoo Ahn, InGyu Choi et al. (+3 more)
Cranes have long been essential equipment for lifting and placing heavy loads in construction projects. This study focuses on the lowering phase of crane operation, the stage in which the load is moved to the desired location. During this phase, a constant challenge exists: the load obstructs the operator's view of the landing point. As a result, operators traditionally have to rely on verbal or gestural instructions from ground personnel, which significantly impacts site safety. To alleviate this constraint, the proposed system incorporates a attachable camera module designed to be attached directly to the load via a suction cup. This module houses a single-board computer, battery, and compact camera. After installation, it streams and processes images of the ground directly below the load in real time to generate installation guidance. Simultaneously, this guidance is transmitted to and monitored by a host computer. Preliminary experiments were conducted by attaching this module to a test object, confirming the feasibility of real-time image acquisition and transmission. This approach has the potential to significantly improve safety on construction sites by providing crane operators with an instant visual reference of hidden landing zones.
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All authors HyoJae Kang, SunWoo Ahn, InGyu Choi, GeonYeong Go, KunWoo Son, Min-Sung Kang
Robotics
Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation
Zhixian Xie, Yu Xiang, Michael Posa et al. (+1 more)
A key challenge in contact-rich dexterous manipulation is the need to jointly reason over global geometry and nonsmooth contact dynamics. End-to-end policies bypass this complexity, but often require large amounts of data and transfer poorly from simulation to reality. We address the limitations with a simple insight: dexterous manipulation is inherently hierarchical–at a high level, a
robot decides where to touch (geometry); at a low level it determines how to move the object through contact dynamics. Building on this insight, we propose a hierarchical RL–MPC framework in which a high-level reinforcement learning (RL) policy predicts a contact intention, a novel object-centric interface that specifies (i) an object-surface contact location and (ii) a post-contact object subgoal pose. Conditioned on the contact intention, a low-level contact-implicit model predictive control (MPC) optimizes local contact modes and real-time (re)plans through contact dynamics to generate
robot actions that robustly move the object toward each subgoal. We evaluate the framework on non-prehensile tasks, including geometry-generalized pushing across diverse object shapes, pivoting/flipping-based object reorientation, and environment-assisted object repositioning. It achieves high success rate with substantially reduced data (40 times fewer RL decision steps and 2 times fewer control steps in the T-pushing comparison), highly robust performance, and zero-shot sim-to-real transfer.
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All authors Zhixian Xie, Yu Xiang, Michael Posa, Wanxin Jin