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Computer Science Robotics

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Thursday 15 January

Robotics

SurfSLAM: Sim-to-Real Underwater Stereo Reconstruction For Real-Time SLAM

Onur Bagoren, Seth Isaacson, Sacchin Sundar et al. (+6 more)

Localization and mapping are core perceptual capabilities for underwater robots. Stereo cameras provide a low-cost means of directly estimating metric depth to support these tasks. However, despite recent advances in stereo depth estimation on land, computing depth from image pairs in underwater scenes remains challenging. In underwater environments, images are degraded by light attenuation, visual artifacts, and dynamic lighting conditions. Furthermore, real-world underwater scenes frequently lack rich texture useful for stereo depth estimation and 3D reconstruction. As a result, stereo estimation networks trained on in-air data cannot transfer directly to the underwater domain. In addition, there is a lack of real-world underwater stereo datasets for supervised training of neural networks. Poor underwater depth estimation is compounded in stereo-based Simultaneous Localization and Mapping (SLAM) algorithms, making it a fundamental challenge for underwater robot perception. To address these challenges, we propose a novel framework that enables sim-to-real training of underwater stereo disparity estimation networks using simulated data and self-supervised finetuning. We leverage our learned depth predictions to develop SurfSLAM, a novel framework for real-time underwater SLAM that fuses stereo cameras with IMU, barometric, and Doppler Velocity Log (DVL) measurements. Lastly, we collect a challenging real-world dataset of shipwreck surveys using an underwater robot. Our dataset features over 24,000 stereo pairs, along with high-quality, dense photogrammetry models and reference trajectories for evaluation. Through extensive experiments, we demonstrate the advantages of the proposed training approach on real-world data for improving stereo estimation in the underwater domain and for enabling accurate trajectory estimation and 3D reconstruction of complex shipwreck sites.
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All authors Onur Bagoren, Seth Isaacson, Sacchin Sundar, Yung-Ching Sun, Anja Sheppard, Haoyu Ma, Abrar Shariff, Ram Vasudevan, Katherine A. Skinner

Robotics

Bidirectional Human-Robot Communication for Physical Human-Robot Interaction

Junxiang Wang, Cindy Wang, Rana Soltani Zarrin et al. (+1 more)

Effective physical human-robot interaction requires systems that are not only adaptable to user preferences but also transparent about their actions. This paper introduces BRIDGE, a system for bidirectional human-robot communication in physical assistance. Our method allows users to modify a robot's planned trajectory – position, velocity, and force – in real time using natural language. We utilize a large language model (LLM) to interpret any trajectory modifications implied by user commands in the context of the planned motion and conversation history. Importantly, our system provides verbal feedback in response to the user, either assuring any resulting changes or posing a clarifying question. We evaluated our method in a user study with 18 older adults across three assistive tasks, comparing BRIDGE to an ablation without verbal feedback and a baseline. Results show that participants successfully used the system to modify trajectories in real time. Moreover, the bidirectional feedback led to significantly higher ratings of interactivity and transparency, demonstrating that the robot's verbal response is critical for a more intuitive user experience. Videos and code can be found on our project website: https://bidir-comm.github.io/
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All authors Junxiang Wang, Cindy Wang, Rana Soltani Zarrin, Zackory Erickson

Robotics · Cross-listed from cs.CV

See Less, Drive Better: Generalizable End-to-End Autonomous Driving via Foundation Models Stochastic Patch Selection

Amir Mallak, Erfan Aasi, Shiva Sreeram et al. (+3 more)

Recent advances in end-to-end autonomous driving show that policies trained on patch-aligned features extracted from foundation models generalize better to Out-of-Distribution (OOD). We hypothesize that due to the self-attention mechanism, each patch feature implicitly embeds/contains information from all other patches, represented in a different way and intensity, making these descriptors highly redundant. We quantify redundancy in such (BLIP2) features via PCA and cross-patch similarity: $90$% of variance is captured by $17/64$ principal components, and strong inter-token correlations are pervasive. Training on such overlapping information leads the policy to overfit spurious correlations, hurting OOD robustness. We present Stochastic-Patch-Selection (SPS), a simple yet effective approach for learning policies that are more robust, generalizable, and efficient. For every frame, SPS randomly masks a fraction of patch descriptors, not feeding them to the policy model, while preserving the spatial layout of the remaining patches. Thus, the policy is provided with different stochastic but complete views of the (same) scene: every random subset of patches acts like a different, yet still sensible, coherent projection of the world. The policy thus bases its decisions on features that are invariant to which specific tokens survive. Extensive experiments confirm that across all OOD scenarios, our method outperforms the state of the art (SOTA), achieving a $6.2$% average improvement and up to $20.4$% in closed-loop simulations, while being $2.4\times$ faster. We conduct ablations over masking rates and patch-feature reorganization, training and evaluating 9 systems, with 8 of them surpassing prior SOTA. Finally, we show that the same learned policy transfers to a physical, real-world car without any tuning.
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All authors Amir Mallak, Erfan Aasi, Shiva Sreeram, Tsun-Hsuan Wang, Daniela Rus, Alaa Maalouf

Robotics · Cross-listed from cs.CL

SurgGoal: Rethinking Surgical Planning Evaluation via Goal-Satisfiability

Ruochen Li, Kun Yuan, Yufei Xia et al. (+5 more)

Surgical planning integrates visual perception, long-horizon reasoning, and procedural knowledge, yet it remains unclear whether current evaluation protocols reliably assess vision-language models (VLMs) in safety-critical settings. Motivated by a goal-oriented view of surgical planning, we define planning correctness via phase-goal satisfiability, where plan validity is determined by expert-defined surgical rules. Based on this definition, we introduce a multicentric meta-evaluation benchmark with valid procedural variations and invalid plans containing order and content errors. Using this benchmark, we show that sequence similarity metrics systematically misjudge planning quality, penalizing valid plans while failing to identify invalid ones. We therefore adopt a rule-based goal-satisfiability metric as a high-precision meta-evaluation reference to assess Video-LLMs under progressively constrained settings, revealing failures due to perception errors and under-constrained reasoning. Structural knowledge consistently improves performance, whereas semantic guidance alone is unreliable and benefits larger models only when combined with structural constraints.
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All authors Ruochen Li, Kun Yuan, Yufei Xia, Yue Zhou, Qingyu Lu, Weihang Li, Youxiang Zhu, Nassir Navab

Robotics

Online sparse Bayesian identification of nonlinear time-varying systems

He Ren, Gaowei Yan, Hang Liu et al. (+3 more)

Sparse regression provides a compact and interpretable route for nonlinear system modeling by selecting a small number of active terms from a candidate dictionary. Most sparse regressors, however, are constructed offline and then used as static predictors. In online operation, changing load, material properties, ambient conditions, or equipment states may alter both the coefficient values and the effective active support within the dictionary. Moreover, a direct recursive update over a rich dictionary may spread the adaptation over many weakly relevant terms, causing an initially sparse model to become increasingly dense. The key problem is therefore to maintain a sparse regressor online, so that it can absorb streaming data while keeping a compact but revisable active structure. This paper develops a Bayesian recursive sparse learning (BRSL) method for online sparse identification over candidate dictionary terms. The coefficient distribution is updated through a Bayesian posterior recursion, where sliding-window likelihood-ratio information recursion incorporates new samples, removes expired samples, and discounts historical information in a unified update. To preserve sparsity during recursion, posterior-guided shrinkage is introduced to suppress weakly supported dictionary terms and revise the active structure according to posterior evidence. The posterior update is performed in a candidate subspace with an adaptive information floor to keep the recursive solve well posed, and a bounded-error relation is given to clarify the influence of shrinkage, residual information, coefficient drift, and information conditioning. The proposed method is evaluated on sparse coefficient tracking and a power-plant-oriented multi-input multi-output (MIMO) nonlinear time-varying identification benchmark.
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All authors He Ren, Gaowei Yan, Hang Liu, Lifeng Cao, Zhijun Zhao, Gang Dang

Robotics

FastStair: Learning to Run Up Stairs with Humanoid Robots

Yan Liu, Tao Yu, Haolin Song et al. (+7 more)

Running up stairs is effortless for humans but remains extremely challenging for humanoid robots due to the simultaneous requirements of high agility and strict stability. Model-free reinforcement learning (RL) can generate dynamic locomotion, yet implicit stability rewards and heavy reliance on task-specific reward shaping tend to result in unsafe behaviors, especially on stairs; conversely, model-based foothold planners encode contact feasibility and stability structure, but enforcing their hard constraints often induces conservative motion that limits speed. We present FastStair, a planner-guided, multi-stage learning framework that reconciles these complementary strengths to achieve fast and stable stair ascent. FastStair integrates a parallel model-based foothold planner into the RL training loop to bias exploration toward dynamically feasible contacts and to pretrain a safety-focused base policy. To mitigate planner-induced conservatism and the discrepancy between low- and high-speed action distributions, the base policy was fine-tuned into speed-specialized experts and then integrated via Low-Rank Adaptation (LoRA) to enable smooth operation across the full commanded-speed range. We deploy the resulting controller on the Oli humanoid robot, achieving stable stair ascent at commanded speeds up to 1.65 m/s and traversing a 33-step spiral staircase (17 cm rise per step) in 12 s, demonstrating robust high-speed performance on long staircases. Notably, the proposed approach served as the champion solution in the Canton Tower Robot Run Up Competition.
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All authors Yan Liu, Tao Yu, Haolin Song, Hongbo Zhu, Nianzong Hu, Yuzhi Hao, Xiuyong Yao, Xizhe Zang, Hua Chen, Jie Zhao

Robotics

CHORAL: Traversal-Aware Planning for Safe and Efficient Heterogeneous Multi-Robot Routing

David Morilla-Cabello and Eduardo Montijano

Monitoring large, unknown, and complex environments with autonomous robots poses significant navigation challenges, where deploying teams of heterogeneous robots with complementary capabilities can substantially improve both mission performance and feasibility. However, effectively modeling how different robotic platforms interact with the environment requires rich, semantic scene understanding. Despite this, existing approaches often assume homogeneous robot teams or focus on discrete task compatibility rather than continuous routing. Consequently, scene understanding is not fully integrated into routing decisions, limiting their ability to adapt to the environment and to leverage each robot's strengths. In this paper, we propose an integrated semantic-aware framework for coordinating heterogeneous robots. Starting from a reconnaissance flight, we build a metric-semantic map using open-vocabulary vision models and use it to identify regions requiring closer inspection and capability-aware paths for each platform to reach them. These are then incorporated into a heterogeneous vehicle routing formulation that jointly assigns inspection tasks and computes robot trajectories. Experiments in simulation and in a real inspection mission with three robotic platforms demonstrate the effectiveness of our approach in planning safer and more efficient routes by explicitly accounting for each platform's navigation capabilities. We release our framework, CHORAL, as open source to support reproducibility and deployment of diverse robot teams.
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Robotics

The impact of tactile sensor configurations on grasp learning efficiency – a comparative evaluation in simulation

Eszter Birtalan and Miklós Koller

Tactile sensors are breaking into the field of robotics to provide direct information related to contact surfaces, including contact events, slip events and even texture identification. These events are especially important for robotic hand designs, including prosthetics, as they can greatly improve grasp stability. Most presently published robotic hand designs, however, implement them in vastly different densities and layouts on the hand surface, often reserving the majority of the available space. We used simulations to evaluate 6 different tactile sensor configurations with different densities and layouts, based on their impact on reinforcement learning. Our two-setup system allows for robust results that are not dependent on the use of a given physics simulator, robotic hand model or machine learning algorithm. Our results show setup-specific, as well as generalized effects across the 6 sensorized simulations, and we identify one configuration as consistently yielding the best performance across both setups. These results could help future research aimed at robotic hand designs, including prostheses.
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Robotics

Proactive Local-Minima-Free Robot Navigation: Blending Motion Prediction with Safe Control

Yifan Xue, Ze Zhang, Knut Åkesson et al. (+1 more)

This work addresses the challenge of safe and efficient mobile robot navigation in complex dynamic environments with concave moving obstacles. Reactive safe controllers like Control Barrier Functions (CBFs) design obstacle avoidance strategies based only on the current states of the obstacles, risking future collisions. To alleviate this problem, we use Gaussian processes to learn barrier functions online from multimodal motion predictions of obstacles generated by neural networks trained with energy-based learning. The learned barrier functions are then fed into quadratic programs using modulated CBFs (MCBFs), a local-minimum-free version of CBFs, to achieve safe and efficient navigation. The proposed framework makes two key contributions. First, it develops a prediction-to-barrier function online learning pipeline. Second, it introduces an autonomous parameter tuning algorithm that adapts MCBFs to deforming, prediction-based barrier functions. The framework is evaluated in both simulations and real-world experiments, consistently outperforming baselines and demonstrating superior safety and efficiency in crowded dynamic environments.
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All authors Yifan Xue, Ze Zhang, Knut Åkesson, Nadia Figueroa

Robotics

A Unified Framework for Kinematic Simulation of Rigid Foldable Structures

Dongwook Kwak, Geonhee Cho, Jiook Chung et al. (+1 more)

Origami-inspired structures with rigid panels now span thick, kirigami, and multi-sheet realizations, making unified kinematic analysis essential. Yet a general method that consolidates their loop constraints has been lacking. We present an automated approach that generates the Pfaffian constraint matrix for arbitrary rigid foldable structures (RFS). From a minimally extended data schema, the tool constructs the facet-hinge graph, extracts a minimum cycle basis that captures all constraints, and assembles a velocity-level constraint matrix via screw theory that encodes coupled rotation and translation loop closure. The framework computes and visualizes deploy and fold motions across diverse RFS while eliminating tedious and error-prone constraint calculations.
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All authors Dongwook Kwak, Geonhee Cho, Jiook Chung, Jinkyu Yang

Robotics

Terrain-Adaptive Mobile 3D Printing with Hierarchical Control

Shuangshan Nors Li and J. Nathan Kutz

Mobile 3D printing on unstructured terrain remains challenging due to the conflict between platform mobility and deposition precision. Existing gantry-based systems achieve high accuracy but lack mobility, while mobile platforms struggle to maintain print quality on uneven ground. We present a framework that tightly integrates AI-driven disturbance prediction with multi-modal sensor fusion and hierarchical hardware control, forming a closed-loop perception-learning-actuation system. The AI module learns terrain-to-perturbation mappings from IMU, vision, and depth sensors, enabling proactive compensation rather than reactive correction. This intelligence is embedded into a three-layer control architecture: path planning, predictive chassis-manipulator coordination, and precision hardware execution. Through outdoor experiments on terrain with slopes and surface irregularities, we demonstrate sub-centimeter printing accuracy while maintaining full platform mobility. This AI-hardware integration establishes a practical foundation for autonomous construction in unstructured environments.
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Robotics · Cross-listed from cs.CV

RAG-3DSG: Enhancing 3D Scene Graphs with Re-Shot Guided Retrieval-Augmented Generation

Yue Chang, Rufeng Chen, Zhaofan Zhang et al. (+3 more)

Open-vocabulary 3D Scene Graph (3DSG) can enhance various downstream tasks in robotics by leveraging structured semantic representations, yet current 3DSG construction methods suffer from semantic inconsistencies caused by noisy cross-image aggregation under occlusions and constrained viewpoints. To mitigate the impact of such inconsistency, we propose RAG-3DSG, which introduces re-shot guided uncertainty estimation. By measuring the semantic consistency between original limited viewpoints and re-shot optimal viewpoints, this method quantifies the underlying semantic ambiguity of each graph object. Based on this quantification, we devise an Object-level Retrieval-Augmented Generation (RAG) that leverages low-uncertainty objects as semantic anchors to retrieve more reliable contextual knowledge, enabling a Vision-Language Model to rectify the predictions of uncertain objects and optimize the final 3DSG. Extensive evaluations across three challenging benchmarks and real-world robot trials demonstrate that RAG-3DSG achieves superior recall and precision, effectively mitigating semantic noise to provide highly reliable scene representations for robotics tasks.
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All authors Yue Chang, Rufeng Chen, Zhaofan Zhang, Yi Chen, Yifan Tian, Sihong Xie

Robotics

CoCoPlan: Adaptive Coordination and Communication for Multi-robot Systems in Dynamic and Unknown Environments

Xintong Zhang, Junfeng Chen, Yuxiao Zhu et al. (+2 more)

Multi-robot systems can greatly enhance efficiency through coordination and collaboration, yet in practice, full-time communication is rarely available and interactions are constrained to close-range exchanges. Existing methods either maintain all-time connectivity, rely on fixed schedules, or adopt pairwise protocols, but none adapt effectively to dynamic spatio-temporal task distributions under limited communication, resulting in suboptimal coordination. To address this gap, we propose CoCoPlan, a unified framework that co-optimizes collaborative task planning and team-wise intermittent communication. Our approach integrates a branch-and-bound architecture that jointly encodes task assignments and communication events, an adaptive objective function that balances task efficiency against communication latency, and a communication event optimization module that strategically determines when, where and how the global connectivity should be re-established. Extensive experiments demonstrate that it outperforms state-of-the-art methods by achieving a 22.4% higher task completion rate, reducing communication overhead by 58.6%, and improving the scalability by supporting up to 100 robots in dynamic environments. Hardware experiments include the complex 2D office environment and large-scale 3D disaster-response scenario.
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All authors Xintong Zhang, Junfeng Chen, Yuxiao Zhu, Bing Luo, Meng Guo

Robotics · Cross-listed from cs.CV

UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow

Nick Truong, Pritam P. Karmokar and William J. Beksi

Underwater imaging is fundamentally challenging due to wavelength-dependent light attenuation, strong scattering from suspended particles, turbidity-induced blur, and non-uniform illumination. These effects impair standard cameras and make ground-truth motion nearly impossible to obtain. On the other hand, event cameras offer microsecond resolution and high dynamic range. Nonetheless, progress on investigating event cameras for underwater environments has been limited due to the lack of datasets that pair realistic underwater optics with accurate optical flow. To address this problem, we introduce the first synthetic underwater benchmark dataset for event-based optical flow derived from physically-based ray-traced RGBD sequences. Using a modern video-to-event pipeline applied to rendered underwater videos, we produce realistic event data streams with dense ground-truth flow, depth, and camera motion. Moreover, we benchmark state-of-the-art learning-based and model-based optical flow prediction methods to understand how underwater light transport affects event formation and motion estimation accuracy. Our dataset establishes a new baseline for future development and evaluation of underwater event-based perception algorithms. The source code and dataset for this project are publicly available at https://robotic-vision-lab.github.io/ueof.
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Robotics

In-the-Wild Compliant Manipulation with UMI-FT

Hojung Choi, Yifan Hou, Chuer Pan et al. (+5 more)

Many manipulation tasks require careful force modulation. With insufficient force the task may fail, while excessive force could cause damage. The high cost, bulky size and fragility of commercial force/torque (F/T) sensors have limited large-scale, force-aware policy learning. We introduce UMI-FT, a handheld data-collection platform that mounts compact, six-axis force/torque sensors on each finger, enabling finger-level wrench measurements alongside RGB, depth, and pose. Using the multimodal data collected from this device, we train an adaptive compliance policy that predicts position targets, grasp force, and stiffness for execution on standard compliance controllers. In evaluations on three contact-rich, force-sensitive tasks (whiteboard wiping, skewering zucchini, and lightbulb insertion), UMI-FT enables policies that reliably regulate external contact forces and internal grasp forces, outperforming baselines that lack compliance or force sensing. UMI-FT offers a scalable path to learning compliant manipulation from in-the-wild demonstrations. We open-source the hardware and software to facilitate broader adoption at:https://umi-ft.github.io/.
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All authors Hojung Choi, Yifan Hou, Chuer Pan, Seongheon Hong, Austin Patel, Xiaomeng Xu, Mark R. Cutkosky, Shuran Song

Robotics · Cross-listed from cs.CV

OT-Drive: Out-of-Distribution Off-Road Traversable Area Segmentation via Optimal Transport

Zhihua Zhao, Guoqiang Li, Chen Min et al. (+1 more)

Reliable traversable area segmentation in unstructured environments is critical for planning and decision-making in autonomous driving. However, existing data-driven approaches often suffer from degraded segmentation performance in out-of-distribution (OOD) scenarios, consequently impairing downstream driving tasks. To address this issue, we propose OT-Drive, an Optimal Transport–driven multi-modal fusion framework. The proposed method formulates RGB and surface normal fusion as a distribution transport problem. Specifically, we design a novel Scene Anchor Generator (SAG) to decompose scene information into the joint distribution of weather, time-of-day, and road type, thereby constructing semantic anchors that can generalize to unseen scenarios. Subsequently, we design an innovative Optimal Transport-based multi-modal fusion module (OT Fusion) to transport RGB and surface normal features onto the manifold defined by the semantic anchors, enabling robust traversable area segmentation under OOD scenarios. Experimental results demonstrate that our method achieves 95.16% mIoU on ORFD OOD scenarios, outperforming prior methods by 6.35%, and 89.79% mIoU on cross-dataset transfer tasks, surpassing baselines by 13.99%.These results indicate that the proposed model can attain strong OOD generalization with only limited training data, substantially enhancing its practicality and efficiency for real-world deployment.
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All authors Zhihua Zhao, Guoqiang Li, Chen Min, Kangping Lu

Wednesday 14 January

Robotics

TurboADMM: A Structure-Exploiting Parallel Solver for Multi-Agent Trajectory Optimization

Yucheng Chen

Multi-agent trajectory optimization with dense interaction networks require solving large coupled QPs at control rates, yet existing solvers fail to simultaneously exploit temporal structure, agent decomposition, and iteration similarity. One usually treats multi-agent problems monolithically when using general-purpose QP solvers (OSQP, MOSEK), which encounter scalability difficulties with agent count. Structure-exploiting solvers (HPIPM) leverage temporal structure through Riccati recursion but can be vulnerable to dense coupling constraints. We introduce TurboADMM, a specialized single-machine QP solver that achieves empirically near linear complexity in agent count through systematic co-design of three complementary components: (1) ADMM decomposition creates per-agent subproblems solvable in parallel, preserving block-tridiagonal structure under dense coupling; (2) Riccati warmstart exploits temporal structure to provide high-quality primal-dual initialization for each agent's QP; (3) parametric QP hotstart \footnote{In the paper, we refer warmstart as the technique that uses the Riccati equation results as auxiliary QP initialization for a single QP solve, while hotstart as reusing the QR factorization across QP solve iterations.}in qpOASES reuses similar KKT system factorizations across ADMM iterations.
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Robotics

From Conflicts to Collisions: A Two-Stage Collision Scenario-Testing Approach for Autonomous Driving Systems

Siyuan Chen, Fuyuan Zhang, Hua Qi et al. (+4 more)

Autonomous driving systems (ADS) are safety-critical and require rigorous testing before public deployment. Simulation-based scenario testing provides a safe and cost-effective alternative to extensive on-road trials, enabling efficient evaluation of ADS under diverse and high-risk conditions. However, existing approaches mainly evaluates the scenarios based on their proximity to collisions and focus on scenarios already close to collision, leaving many other hazardous situations unexplored. To bridge this, we introduce a collision-related concept of conflict as an intermediate search target and propose a two-stage scenario testing framework that first searches for conflicts and then mutates these conflict scenarios to induce actual collisions. Evaluated on Baidu Apollo, our approach reveals up to 12 distinct collision types in a single run, doubling the diversity discovered by state-of-the-art baselines while requiring fewer simulations thanks to conflict-targeted mutations. These results show that using conflicts as intermediate objectives broadens the search horizon and significantly improves the efficiency and effectiveness of ADS safety evaluation.
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All authors Siyuan Chen, Fuyuan Zhang, Hua Qi, Lei Ma, Tomoyuki Tsuchiya, Michio Hayashi, Manabu Okada

Robotics · Cross-listed from cs.AI

Online parameter estimation for the Crazyflie quadcopter through an EM algorithm

Yanhua Zhao

Drones are becoming more and more popular nowadays. They are small in size, low in cost, and reliable in operation. They contain a variety of sensors and can perform a variety of flight tasks, reaching places that are difficult or inaccessible for humans. Earthquakes damage a lot of infrastructure, making it impossible for rescuers to reach some areas. But drones can help. Many amateur and professional photographers like to use drones for aerial photography. Drones play a non-negligible role in agriculture and transportation too. Drones can be used to spray pesticides, and they can also transport supplies. A quadcopter is a four-rotor drone and has been studied in this paper. In this paper, random noise is added to the quadcopter system and its effects on the drone system are studied. An extended Kalman filter has been used to estimate the state based on noisy observations from the sensor. Based on a SDE system, a linear quadratic Gaussian controller has been implemented. The expectation maximization algorithm has been applied for parameter estimation of the quadcopter. The results of offline parameter estimation and online parameter estimation are presented. The results show that the online parameter estimation has a slightly larger range of convergence values than the offline parameter estimation.
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Robotics

SyncTwin: Fast Digital Twin Construction and Synchronization for Safe Robotic Manipulation

Ruopeng Huang, Boyu Yang, Wenlong Gui et al. (+3 more)

Accurate and safe robotic manipulation under dynamic and visually occluded conditions remains a core challenge in real-world deployment. We introduce SyncTwin, a novel digital twin framework that unifies fast 3D scene reconstruction and real-to-sim synchronization for robust and safety-aware robotic manipulation in such environments. In the offline stage, we employ VGGT to rapidly reconstruct object-level 3D assets from RGB images, forming a reusable geometry library. During execution, SyncTwin continuously synchronizes the digital twin by tracking real-world object states via point cloud segmentation updates and aligning them through colored-ICP registration. The synchronized twin enables motion planners to compute collision-free and dynamically feasible trajectories in simulation, which are safely executed on the real robot through a closed real-to-sim-to-real loop. Experiments in dynamic and occluded scenes show that SyncTwin improves manipulation performance and motion safety, demonstrating the effectiveness of digital twin synchronization for real-world robotic execution. The video demos and code can be found on the project website: https://sync-twin.github.io/.
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All authors Ruopeng Huang, Boyu Yang, Wenlong Gui, Jeremy Morgan, Erdem Biyik, Jiachen Li

Robotics

How Human Motion Prediction Quality Shapes Social Robot Navigation Performance in Constrained Spaces

Andrew Stratton, Phani Teja Singamaneni, Pranav Goyal et al. (+2 more)

Motivated by the vision of integrating mobile robots closer to humans in warehouses, hospitals, manufacturing plants, and the home, we focus on robot navigation in dynamic and spatially constrained environments. Ensuring human safety, comfort, and efficiency in such settings requires that robots are endowed with a model of how humans move around them. Human motion prediction around robots is especially challenging due to the stochasticity of human behavior, differences in user preferences, and data scarcity. In this work, we perform a methodical investigation of the effects of human motion prediction quality on robot navigation performance, as well as human productivity and impressions. We design a scenario involving robot navigation among two human subjects in a constrained workspace and instantiate it in a user study ($N=80$) involving two different robot platforms, conducted across two sites from different world regions. Key findings include evidence that: 1) the widely adopted average displacement error is not a reliable predictor of robot navigation performance and human impressions; 2) the common assumption of human cooperation breaks down in constrained environments, with users often not reciprocating robot cooperation, and causing performance degradations; 3) more efficient robot navigation often comes at the expense of human efficiency and comfort.
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All authors Andrew Stratton, Phani Teja Singamaneni, Pranav Goyal, Rachid Alami, Christoforos Mavrogiannis

Robotics

Interprofessional and Agile Development of Mobirobot: A Socially Assistive Robot for Pediatric Therapy Across Clinical and Therapeutic Settings

Leonie Dyck, Aiko Galetzka, Maximilian Noller et al. (+17 more)

Introduction: Socially assistive robots hold promise for enhancing therapeutic engagement in paediatric clinical settings. However, their successful implementation requires not only technical robustness but also context-sensitive, co-designed solutions. This paper presents Mobirobot, a socially assistive robot developed to support mobilisation in children recovering from trauma, fractures, or depressive disorders through personalised exercise programmes. Methods: An agile, human-centred development approach guided the iterative design of Mobirobot. Multidisciplinary clinical teams and end users were involved throughout the co-development process, which focused on early integration into real-world paediatric surgical and psychiatric settings. The robot, based on the NAO platform, features a simple setup, adaptable exercise routines with interactive guidance, motivational dialogue, and a graphical user interface (GUI) for monitoring and no-code system feedback. Results: Deployment in hospital environments enabled the identification of key design requirements and usability constraints. Stakeholder feedback led to refinements in interaction design, movement capabilities, and technical configuration. A feasibility study is currently underway to assess acceptance, usability, and perceived therapeutic benefit, with data collection including questionnaires, behavioural observations, and staff-patient interviews. Discussion: Mobirobot demonstrates how multiprofessional, stakeholder-led development can yield a socially assistive system suited for dynamic inpatient settings. Early-stage findings underscore the importance of contextual integration, robustness, and minimal-intrusion design. While challenges such as sensor limitations and patient recruitment remain, the platform offers a promising foundation for further research and clinical application.
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All authors Leonie Dyck, Aiko Galetzka, Maximilian Noller, Anna-Lena Rinke, Jutta Bormann, Jekaterina Miller, Michelle Hochbaum, Julia Siemann, Jördis Alboth, Andre Berwinkel, Johanna Luz, Britta Kley-Zobel, Marcine Cyrys, Nora Flöttmann, Ariane Vogeler, Mariia Melnikova, Ira-Katharina Petras, Michael Siniatchkin, Winfried Barthlen, Anna-Lisa Vollmer

Robotics · Cross-listed from cs.CV

LCF3D: A Robust and Real-Time Late-Cascade Fusion Framework for 3D Object Detection in Autonomous Driving

Carlo Sgaravatti, Riccardo Pieroni, Matteo Corno et al. (+3 more)

Accurately localizing 3D objects like pedestrians, cyclists, and other vehicles is essential in Autonomous Driving. To ensure high detection performance, Autonomous Vehicles complement RGB cameras with LiDAR sensors, but effectively combining these data sources for 3D object detection remains challenging. We propose LCF3D, a novel sensor fusion framework that combines a 2D object detector on RGB images with a 3D object detector on LiDAR point clouds. By leveraging multimodal fusion principles, we compensate for inaccuracies in the LiDAR object detection network. Our solution combines two key principles: (i) late fusion, to reduce LiDAR False Positives by matching LiDAR 3D detections with RGB 2D detections and filtering out unmatched LiDAR detections; and (ii) cascade fusion, to recover missed objects from LiDAR by generating new 3D frustum proposals corresponding to unmatched RGB detections. Experiments show that LCF3D is beneficial for domain generalization, as it turns out to be successful in handling different sensor configurations between training and testing domains. LCF3D achieves significant improvements over LiDAR-based methods, particularly for challenging categories like pedestrians and cyclists in the KITTI dataset, as well as motorcycles and bicycles in nuScenes. Code can be downloaded from: https://github.com/CarloSgaravatti/LCF3D.
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All authors Carlo Sgaravatti, Riccardo Pieroni, Matteo Corno, Sergio M. Savaresi, Luca Magri, Giacomo Boracchi

Robotics · Cross-listed from cs.CV

Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

Chi-Pin Huang, Yunze Man, Zhiding Yu et al. (+4 more)

Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain-of-thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy reasoning traces. We propose Fast-ThinkAct, an efficient reasoning framework that achieves compact yet performant planning through verbalizable latent reasoning. Fast-ThinkAct learns to reason efficiently with latent CoTs by distilling from a teacher, driven by a preference-guided objective to align manipulation trajectories that transfers both linguistic and visual planning capabilities for embodied control. This enables reasoning-enhanced policy learning that effectively connects compact reasoning to action execution. Extensive experiments across diverse embodied manipulation and reasoning benchmarks demonstrate that Fast-ThinkAct achieves strong performance with up to 89.3% reduced inference latency over state-of-the-art reasoning VLAs, while maintaining effective long-horizon planning, few-shot adaptation, and failure recovery.
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All authors Chi-Pin Huang, Yunze Man, Zhiding Yu, Min-Hung Chen, Jan Kautz, Yu-Chiang Frank Wang, Fu-En Yang

Robotics · Cross-listed from cs.CV

Sim2real Image Translation Enables Viewpoint-Robust Policies from Fixed-Camera Datasets

Jeremiah Coholich, Justin Wit, Robert Azarcon et al. (+1 more)

Vision-based policies for robot manipulation have achieved significant recent success, but are still brittle to distribution shifts such as camera viewpoint variations. Robot demonstration data is scarce and often lacks appropriate variation in camera viewpoints. Simulation offers a way to collect robot demonstrations at scale with comprehensive coverage of different viewpoints, but presents a visual sim2real challenge. To bridge this gap, we propose MANGO – an unpaired image translation method with a novel segmentation-conditioned InfoNCE loss, a highly-regularized discriminator design, and a modified PatchNCE loss. We find that these elements are crucial for maintaining viewpoint consistency during sim2real translation. When training MANGO, we only require a small amount of fixed-camera data from the real world, but show that our method can generate diverse unseen viewpoints by translating simulated observations. In this setting, MANGO outperforms all other image translation methods we tested. In certain real-world tabletop manipulation tasks, MANGO augmentation increases shifted-view success rates by over 40 percentage points compared to policies trained without augmentation.
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All authors Jeremiah Coholich, Justin Wit, Robert Azarcon, Zsolt Kira

Robotics

Multimodal Signal Processing For Thermo-Visible-Lidar Fusion In Real-time 3D Semantic Mapping

Jiajun Sun, Yangyi Ou, Haoyuan Zheng et al. (+2 more)

In complex environments, autonomous robot navigation and environmental perception pose higher requirements for SLAM technology. This paper presents a novel method for semantically enhancing 3D point cloud maps with thermal information. By first performing pixel-level fusion of visible and infrared images, the system projects real-time LiDAR point clouds onto this fused image stream. It then segments heat source features in the thermal channel to instantly identify high temperature targets and applies this temperature information as a semantic layer on the final 3D map. This approach generates maps that not only have accurate geometry but also possess a critical semantic understanding of the environment, making it highly valuable for specific applications like rapid disaster assessment and industrial preventive maintenance.
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All authors Jiajun Sun, Yangyi Ou, Haoyuan Zheng, Chao yang, Yue Ma

Robotics

Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations

Wei-Jin Huang, Yue-Yi Zhang, Yi-Lin Wei et al. (+5 more)

Enabling humanoid robots to physically interact with humans is a critical frontier, but progress is hindered by the scarcity of high-quality Human-Humanoid Interaction (HHoI) data. While leveraging abundant Human-Human Interaction (HHI) data presents a scalable alternative, we first demonstrate that standard retargeting fails by breaking the essential contacts. We address this with PAIR (Physics-Aware Interaction Retargeting), a contact-centric, two-stage pipeline that preserves contact semantics across morphology differences to generate physically consistent HHoI data. This high-quality data, however, exposes a second failure: conventional imitation learning policies merely mimic trajectories and lack interactive understanding. We therefore introduce D-STAR (Decoupled Spatio-Temporal Action Reasoner), a hierarchical policy that disentangles when to act from where to act. In D-STAR, Phase Attention (when) and a Multi-Scale Spatial module (where) are fused by the diffusion head to produce synchronized whole-body behaviors beyond mimicry. By decoupling these reasoning streams, our model learns robust temporal phases without being distracted by spatial noise, leading to responsive, synchronized collaboration. We validate our framework through extensive and rigorous simulations, demonstrating significant performance gains over baseline approaches and a complete, effective pipeline for learning complex whole-body interactions from HHI data.
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All authors Wei-Jin Huang, Yue-Yi Zhang, Yi-Lin Wei, Zhi-Wei Xia, Juantao Tan, Yuan-Ming Li, Zhilin Zhao, Wei-Shi Zheng

Robotics

CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion

Ralf Römer, Yi Zhang, Yuming Li et al. (+1 more)

To teach robots complex manipulation tasks, a common approach is to fine-tune a pre-trained vision-language-action model (VLA) on task-specific data. However, since this recipe updates existing representations, it is unsuitable for long-term operation in the real world, where robots must continually adapt to new tasks and environments while retaining the knowledge they have already acquired. Existing continual learning methods for robotics commonly require storing previous data (exemplars), struggle with long task sequences, or rely on task identifiers for deployment. To address these limitations, we propose CLARE, a general, parameter-efficient framework for exemplar-free continual learning with VLAs. CLARE introduces lightweight modular adapters into selected VLA modules and autonomously expands the model only where necessary when learning a new task, guided by layer-wise feature similarity. During deployment, an autoencoder-based routing mechanism dynamically activates the most relevant adapters without requiring task labels. Through extensive experiments on the LIBERO benchmark and five real-world tasks, we show that CLARE achieves high performance on new tasks without catastrophic forgetting of earlier tasks, significantly outperforming even exemplar-based methods. Code, data, and videos are available at our website: https://tum-lsy.github.io/clare.
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All authors Ralf Römer, Yi Zhang, Yuming Li, Angela P. Schoellig

Robotics

Data Scaling for Navigation in Unknown Environments

Lauri Suomela, Naoki Takahata, Sasanka Kuruppu Arachchige et al. (+2 more)

Generalization of imitation-learned navigation policies to environments unseen in training remains a major challenge. We address this by conducting the first large-scale study of how data quantity and data diversity affect real-world generalization in end-to-end, map-free visual navigation. Using a curated 4,565-hour crowd-sourced dataset collected across 161 locations in 35 countries, we train policies for point goal navigation and evaluate their closed-loop control performance on sidewalk robots operating in four countries, covering 125 km of autonomous driving. Our results show that large-scale training data enables zero-shot navigation in unknown environments, approaching the performance of policies trained with environment-specific demonstrations. Critically, we find that data diversity is far more important than data quantity. Doubling the number of geographical locations in a training set decreases navigation errors by ~15%, while performance benefit from adding data from existing locations saturates with very little data. We also observe that, with noisy crowd-sourced data, simple regression-based models outperform generative and sequence-based architectures. We release our policies, evaluation setup and example videos at https://lasuomela.github.io/navigation_scaling/.
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All authors Lauri Suomela, Naoki Takahata, Sasanka Kuruppu Arachchige, Harry Edelman, Joni-Kristian Kämäräinen

Robotics

ReflexDiffusion: Reflection-Enhanced Trajectory Planning for High-lateral-acceleration Scenarios in Autonomous Driving

Xuemei Yao, Xiao Yang, Jianbin Sun et al. (+5 more)

Generating safe and reliable trajectories for autonomous vehicles in long-tail scenarios remains a significant challenge, particularly for high-lateral-acceleration maneuvers such as sharp turns, which represent critical safety situations. Existing trajectory planners exhibit systematic failures in these scenarios due to data imbalance. This results in insufficient modelling of vehicle dynamics, road geometry, and environmental constraints in high-risk situations, leading to suboptimal or unsafe trajectory prediction when vehicles operate near their physical limits. In this paper, we introduce ReflexDiffusion, a novel inference-stage framework that enhances diffusion-based trajectory planners through reflective adjustment. Our method introduces a gradient-based adjustment mechanism during the iterative denoising process: after each standard trajectory update, we compute the gradient between the conditional and unconditional noise predictions to explicitly amplify critical conditioning signals, including road curvature and lateral vehicle dynamics. This amplification enforces strict adherence to physical constraints, particularly improving stability during high-lateral-acceleration maneuvers where precise vehicle-road interaction is paramount. Evaluated on the nuPlan Test14-hard benchmark, ReflexDiffusion achieves a 14.1% improvement in driving score for high-lateral-acceleration scenarios over the state-of-the-art (SOTA) methods. This demonstrates that inference-time trajectory optimization can effectively compensate for training data sparsity by dynamically reinforcing safety-critical constraints near handling limits. The framework's architecture-agnostic design enables direct deployment to existing diffusion-based planners, offering a practical solution for improving autonomous vehicle safety in challenging driving conditions.
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All authors Xuemei Yao, Xiao Yang, Jianbin Sun, Liuwei Xie, Xuebin Shao, Xiyu Fang, Hang Su, Kewei Yang