Robotics
A Survey of Medical Drones from Flight Dynamics, Guidance, Navigation, and Control Perspectives
Roshan Kumar Chhetri, Sarocha Jetawatthana and Thanakorn Khamvilai
The integration of drones into the medical field has revolutionized healthcare delivery by enabling rapid transportation of medical supplies, organs, and even emergency assistance in remote or disaster-stricken areas. While other survey papers focus on the healthcare supply chain, operations, and medical emergency response aspects, this paper provides a comprehensive review of medical drones from the perspectives of flight dynamics and guidance,
navigation, and control (GNC) systems. We first discuss the medical aerial delivery mission requirements and suitable uncrewed aerial system (UAS) configurations. We then address payload container design and optimization, and its effect on supplies and overall flight dynamics. We also explore the fundamental principles of GNC in the context of medical drone operations, highlighting key challenges arising from vibration, air temperature, pressure, and humidity, which affect the quality of medical supplies. The paper examines various GNC
algorithms that can mitigate these challenges, as well as the
algorithms' limitations. With these considerations, this survey aims to provide insights into optimizing GNC frameworks for medical drones, emphasizing research gaps and directions to improve real-world healthcare applications.
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Robotics · Cross-listed from cs.CV
Low-Back Pain Physical Rehabilitation by Movement Analysis in Clinical Trial
Sao Mai Nguyen
To allow the development and assessment of physical rehabilitation by an intelligent tutoring system, we propose a medical
dataset of clinical patients carrying out low back-pain rehabilitation exercises and
benchmark on state of the art human movement analysis
algorithms. This
dataset is valuable because it includes rehabilitation motions in a clinical setting with patients in their rehabilitation program. This paper introduces the Keraal
dataset, a clinically collected
dataset to enable intelligent tutoring systems (ITS) for rehabilitation. It addresses four challenges in exercise monitoring: motion assessment, error recognition, spatial localization, temporal localization
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Robotics · Cross-listed from cs.LG
A Review of Online Diffusion Policy RL Algorithms for Scalable Robotic Control
Wonhyeok Choi, Shutong Ding, Minwoo Choi et al. (+5 more)
Diffusion policies have emerged as a powerful approach for robotic control, demonstrating superior expressiveness in modeling
multimodal action distributions compared to conventional policy networks. However, their integration with online reinforcement learning remains challenging due to fundamental incompatibilities between
diffusion model training objectives and standard RL policy improvement mechanisms. This paper presents the first comprehensive review and empirical analysis of current Online
Diffusion Policy Reinforcement Learning (Online DPRL)
algorithms for scalable robotic control systems. We propose a novel taxonomy that categorizes existing approaches into four distinct families–Action-Gradient, Q-Weighting, Proximity-Based, and Backpropagation Through Time (BPTT) methods–based on their policy improvement mechanisms. Through extensive experiments on a unified NVIDIA Isaac Lab
benchmark encompassing 12 diverse robotic tasks, we systematically evaluate representative
algorithms across five critical dimensions: task diversity, parallelization capability,
diffusion step scalability, cross-embodiment generalization, and environmental robustness. Our analysis identifies key findings regarding the fundamental trade-offs inherent in each algorithmic family, particularly concerning sample efficiency and scalability. Furthermore, we reveal critical computational and algorithmic bottlenecks that currently limit the practical deployment of online DPRL. Based on these findings, we provide concrete guidelines for
algorithm selection tailored to specific operational constraints and outline promising future research directions to advance the field toward more general and scalable robotic learning systems.
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All authors Wonhyeok Choi, Shutong Ding, Minwoo Choi, Jungwan Woo, Kyumin Hwang, Jaeyeul Kim, Ye Shi, Sunghoon Im
Robotics · Cross-listed from eess.SY
AMC26: High-performance DOb for robust position control
Emre Sariyildiz
This paper presents a new HPDOb that significantly improves disturbance estimation accuracy and robustness in motion control systems, surpassing the capabilities of conventional DObs. The proposed observer is analysed and synthesised in the discrete-time domain, providing a realistic representation of their dynamic behaviour and enabling enhanced controller design for practical applications. The core contribution of the HPDOb is a novel synthesis method that incorporates higher-order truncation error dynamics into disturbance estimation. Unlike conventional DObs, which are limited to zero-order truncation error, the HPDOb achieves first-order truncation error, yielding markedly improved estimation accuracy and robustness against disturbances in motion control systems. Simulation and experiments verify the stability and performance of HPDOb.
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Robotics
Learning and Optimizing the Efficacy of Spatio-Temporal Task Allocation under Temporal and Resource Constraints
Jiazhen Liu, Glen Neville, Jinwoo Park et al. (+2 more)
Complex multi-
robot missions often require heterogeneous teams to jointly optimize task allocation, scheduling, and path planning to improve team performance under strict constraints. We formalize these complexities into a new class of problems, dubbed Spatio-Temporal Efficacy-optimized Allocation for Multi-
robot systems (STEAM). STEAM builds upon trait-based frameworks that model
robots using their capabilities (e.g., payload and speed), but goes beyond the typical binary success-failure model by explicitly modeling the efficacy of allocations as trait-efficacy maps. These maps encode how the aggregated capabilities assigned to a task determine performance. Further, STEAM accommodates spatio-temporal constraints, including a user-specified time budget (i.e., maximum makespan). To solve STEAM problems, we contribute a novel
algorithm named Efficacy-optimized Incremental Task Allocation Graph Search (E-ITAGS) that simultaneously optimizes task performance and respects time budgets by interleaving task allocation, scheduling, and path planning. Motivated by the fact that trait-efficacy maps are difficult, if not impossible, to specify, E-ITAGS efficiently learns them using a realizability-aware active learning module. Our approach is realizability-aware since it explicitly accounts for the fact that not all combinations of traits are realizable by the
robots available during learning. Further, we derive experimentally-validated bounds on E-ITAGS' suboptimality with respect to efficacy. Detailed numerical simulations and experiments using an emergency response domain demonstrate that E-ITAGS generates allocations of higher efficacy compared to baselines, while respecting resource and spatio-temporal constraints. We also show that our active learning approach is sample efficient and establishes a principled tradeoff between data and computational efficiency.
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All authors Jiazhen Liu, Glen Neville, Jinwoo Park, Sonia Chernova, Harish Ravichandar
Robotics
InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
Junhao Cai, Zetao Cai, Jiafei Cao et al. (+39 more)
Prevalent
Vision-
Language-Action (VLA) models are typically built upon
Multimodal Large
Language Models (MLLMs) and demonstrate exceptional proficiency in semantic understanding, but they inherently lack the capability to deduce physical world dynamics. Consequently, recent approaches have shifted toward World Models, typically formulated via
video prediction; however, these methods often suffer from a lack of semantic grounding and exhibit brittleness in the presence of
video prediction errors. To synergize semantic understanding with dynamic predictive capabilities, we present InternVLA-A1. This model employs a unified Mixture-of-
Transformers architecture, coordinating three experts for scene understanding, visual foresight generation, and action execution. These components interact seamlessly through a unified masked self
attention mechanism. Building upon InternVL3 and Qwen3-VL, we instantiate InternVLA-A1 at 2B and 3B parameter scales. We pre-train these models on heterogeneous data sources over real-world
robot data, synthetic simulation data, and human
videos, covering over 692M frames. This hybrid training strategy effectively harnesses the diversity of synthetic simulation data while minimizing the sim-to-real gap. We evaluated InternVLA-A1 on 12 real-world robotic tasks and a simulation
benchmark. The results show that InternVLA-A1 consistently outperforms prior leading models: compared with pi0.5, it achieves +4.4% on static manipulation tasks and +2.6% on the RoboTwin 2.0 simulation
benchmark, and delivers a +26.7% boost on dynamic manipulation tasks.
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All authors Junhao Cai, Zetao Cai, Jiafei Cao, Yilun Chen, Zeyu He, Lei Jiang, Hang Li, Hengjie Li, Yang Li, Yufei Liu, Yanan Lu, Qi Lv, Haoxiang Ma, Jiangmiao Pang, Yu Qiao, Zherui Qiu, Yanqing Shen, Xu Shi, Yang Tian, Bolun Wang, Hanqing Wang, Jiaheng Wang, Tai Wang, Xueyuan Wei, Chao Wu, Yiman Xie, Boyang Xing, Yuqiang Yang, Yuyin Yang, Qiaojun Yu, Feng Yuan, Jia Zeng, Jingjing Zhang, Shenghan Zhang, Shi Zhang, Zhuoma Zhaxi, Bowen Zhou, Yuanzhen Zhou, Yunsong Zhou, Hongrui Zhu, Yangkun Zhu, Yuchen Zhu
Robotics
CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding
Chenyang Ma, Kai Lu, Guangyu Yang et al. (+6 more)
Current work on
robot failure detection and correction typically operates in a post hoc manner, analyzing errors and applying corrections only after failures occur. This work introduces CycleVLA, a system that equips
Vision-
Language-Action models (VLAs) with proactive self-correction, the capability to anticipate incipient failures and recover before they fully manifest during execution. CycleVLA achieves this by integrating a progress-aware VLA that flags critical subtask transition points where failures most frequently occur, a VLM-based failure predictor and planner that triggers subtask backtracking upon predicted failure, and a test-time scaling strategy based on Minimum Bayes Risk (MBR) decoding to improve retry success after backtracking. Extensive experiments on the LIBERO and LIBERO-Plus simulation
benchmarks show that CycleVLA surpasses the state-of-the-art VLA {\pi}0.5, improves success rates by correcting execution failures across VLAs of varying capability, from under-trained ones to fully converged policies, and that MBR serves as an effective zero-shot test-time scaling strategy for VLAs. On a real
robot, CycleVLA reaches a 91% average success rate on one precise and two long-horizon manipulation tasks. We further conduct stress tests with multiple manually injected perturbations (e.g., swapping in a distractor at the expected location while relocating the true target object mid-execution), where CycleVLA corrects ~80% of injected failures and maintains success rates comparable to unperturbed execution. Project Page: https://dannymcy.github.io/cyclevla/
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All authors Chenyang Ma, Kai Lu, Guangyu Yang, Jiuming Liu, Shitong Xu, Bill Byrne, Ioannis Havoutis, Niki Trigoni, Andrew Markham
Robotics
Differential Barometric Altimetry for Submeter Vertical Localization and Floor Recognition Indoors
Yuhang Zhang and Sören Schwertfeger
Accurate altitude estimation and reliable floor recognition are critical for mobile
robot localization and
navigation within complex multi-storey environments. In this paper, we present a robust, low-cost vertical estimation framework leveraging differential barometric sensing integrated within a fully ROS-compliant software package. Our system simultaneously publishes real-time altitude data from both a stationary base station and a mobile sensor, enabling precise and drift-free vertical localization. Empirical evaluations conducted in challenging scenarios – such as fully enclosed stairwells and elevators, demonstrate that our proposed barometric pipeline achieves sub-meter vertical accuracy (RMSE: 0.29 m) and perfect (100%) floor-level identification. In contrast, our results confirm that standalone height estimates, obtained solely from visual- or
LiDAR-based SLAM odometry, are insufficient for reliable vertical localization. The proposed ROS-compatible barometric module thus provides a practical and cost-effective solution for robust vertical awareness in real-world robotic deployments. The implementation of our method is released as open source at https://github.com/witsir/differential-barometric.
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Robotics
SingingBot: An Avatar-Driven System for Robotic Face Singing Performance
Zhuoxiong Xu, Xuanchen Li, Yuhao Cheng et al. (+3 more)
Equipping robotic faces with singing capabilities is crucial for empathetic Human-
Robot Interaction. However, existing robotic face driving research primarily focuses on conversations or mimicking static expressions, struggling to meet the high demands for continuous emotional expression and coherence in singing. To address this, we propose a novel avatar-driven framework for appealing robotic singing. We first leverage portrait
video generation models embedded with extensive human priors to synthesize vivid singing avatars, providing reliable expression and
emotion guidance. Subsequently, these facial features are transferred to the
robot via semantic-oriented mapping functions that span a wide expression space. Furthermore, to quantitatively evaluate the emotional richness of robotic singing, we propose the
Emotion Dynamic Range metric to measure the emotional breadth within the Valence-Arousal space, revealing that a broad emotional spectrum is crucial for appealing performances. Comprehensive experiments prove that our method achieves rich emotional expressions while maintaining lip-audio synchronization, significantly outperforming existing approaches.
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All authors Zhuoxiong Xu, Xuanchen Li, Yuhao Cheng, Fei Xu, Yichao Yan, Xiaokang Yang
Robotics
Vision-Based Early Fault Diagnosis and Self-Recovery for Strawberry Harvesting Robots
Meili Sun, Chunjiang Zhao, Lichao Yang et al. (+3 more)
Strawberry-harvesting
robots faced challenges such as poor visual perception, gripper misalignment, empty grasp/misgrasp, and slippage, which reduced harvesting stability and efficiency.To overcome these issues, this paper proposes a visual fault diagnosis and self-recovery framework. An end-to-end SRR-Net achieved unified perception and fault diagnosis through joint detection,
segmentation, and ripeness regression of the fruit and gripper. Leveraging this integrated perception, a relative error compensation method driven by simultaneous target-gripper detection was designed to correct positional misalignments exceeding the tolerance threshold. A micro-optical camera integrated within the end-effector delivered real-time visual feedback. Based on the micro-optical camera, a MobileNet V3-Small classifier was utilized for grasp adjustment during the deflating stage, enabling the early abort of the harvesting cycle in cases of empty grasp/misgrasps. Furthermore, a time-series LSTM classifier was applied during the snap-off stage to predict strawberry slippage. Based on these predictions, the system executed re-inflation and a secondary snap-off attempt for slipping strawberries, or aborted the cycle for slipped strawberries. Experiments demonstrated that the mean absolute errors between the end-effector and the picking point were reduced to 3.12 mm and 4.06 mm from 11.50 mm and 5.25 mm along the x- and y-axes, respectively, at the cost of a time increment of 0.64 $pm$ 0.24 s. The grasp adjustment module reduced the grasping phase by approximately 0.5 s and avoided empty-placement for failure cases. The strawberry slip prediction module handled slipped cases with an 88.89% success rate, saving approximately 4.00 s per harvesting cycle for failure cases. Also, it achieved an 81.25% recovery rate for slipping strawberries, requiring additional 0.63 s for re-grasping.
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All authors Meili Sun, Chunjiang Zhao, Lichao Yang, Hao Liu, Shimin Hu, Ya Xiong
Robotics · Cross-listed from cs.HC
Realistic adversarial scenario generation via human-like pedestrian model for autonomous vehicle control parameter optimisation
Yueyang Wang, Mehmet Dogar, Russell Darling et al. (+1 more)
Autonomous vehicles (AVs) are rapidly advancing and are expected to play a central role in future mobility. Ensuring their safe deployment requires reliable interaction with other road users, not least pedestrians. Direct testing on public roads is costly and unsafe for rare but critical interactions, making simulation a practical alternative. Within simulation-based testing,
adversarial scenarios are widely used to probe safety limits, but many prioritise difficulty over realism, producing exaggerated behaviours which may result in AV controllers that are overly conservative. We propose an alternative method, instead using a cognitively inspired pedestrian model featuring both inter-individual and intra-individual variability to generate behaviourally plausible
adversarial scenarios. We provide a proof of concept demonstration of this method's potential for AV control optimisation, in closed-loop testing and tuning of an AV controller. Our results show that replacing the rule-based CARLA pedestrian with the human-like model yields more realistic gap acceptance patterns and smoother vehicle decelerations. Unsafe interactions occur only for certain pedestrian individuals and conditions, underscoring the importance of human variability in AV testing.
Adversarial scenarios generated by this model can be used to optimise AV control towards safer and more efficient behaviour. Overall, this work illustrates how incorporating human-like road user models into simulation-based
adversarial testing can enhance the credibility of AV evaluation and provide a practical basis to behaviourally informed controller optimisation.
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All authors Yueyang Wang, Mehmet Dogar, Russell Darling, Gustav Markkula
Robotics
Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot
Chenghao Yin, Da Huang, Di Yang et al. (+16 more)
The development of robust and generalizable
robot learning models is critically contingent upon the availability of large-scale, diverse training data and reliable evaluation
benchmarks. Collecting data in the physical world poses prohibitive costs and scalability challenges, and prevailing simulation
benchmarks frequently suffer from fragmentation, narrow scope, or insufficient fidelity to enable effective sim-to-real transfer. To address these challenges, we introduce Genie Sim 3.0, a unified simulation platform for robotic manipulation. We present Genie Sim Generator, a large
language model (LLM)-powered tool that constructs high-fidelity scenes from natural
language instructions. Its principal strength resides in rapid and multi-dimensional generalization, facilitating the synthesis of diverse environments to support scalable data collection and robust policy evaluation. We introduce the first
benchmark that pioneers the application of LLM for automated evaluation. It leverages LLM to mass-generate evaluation scenarios and employs
Vision-
Language Model (VLM) to establish an automated assessment pipeline. We also release an open-source
dataset comprising more than 10,000 hours of synthetic data across over 200 tasks. Through systematic experimentation, we validate the robust zero-shot sim-to-real transfer capability of our open-source
dataset, demonstrating that synthetic data can server as an effective substitute for real-world data under controlled conditions for scalable policy training. For code and
dataset details, please refer to: https://github.com/AgibotTech/genie_sim.
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All authors Chenghao Yin, Da Huang, Di Yang, Jichao Wang, Nanshu Zhao, Chen Xu, Wenjun Sun, Linjie Hou, Zhijun Li, Junhui Wu, Zhaobo Liu, Zhen Xiao, Sheng Zhang, Lei Bao, Rui Feng, Zhenquan Pang, Jiayu Li, Qian Wang, Maoqing Yao
Robotics · Cross-listed from cs.CV
VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysis
Aly R. Elkammar, Karim M. Gamaleldin and Catherine M. Elias
Pedestrian Intention prediction is one of the key technologies in the transition from level 3 to level 4 autonomous driving. To understand pedestrian crossing behaviour, several elements and features should be taken into consideration to make the roads of tomorrow safer for everybody. We introduce a
transformer /
video vision transformer based
algorithm of different sizes which uses different data modalities .We evaluated our
algorithms on popular pedestrian behaviour
dataset, JAAD, and have reached SOTA performance and passed the SOTA in metrics like Accuracy, AUC and F1-score. The advantages brought by different model design choices are investigated via extensive ablation studies.
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Robotics
Deep Robust Koopman Learning from Noisy Data
Aditya Singh, Rajpal Singh and Jishnu Keshavan
Koopman operator theory has emerged as a leading data-driven approach that relies on a judicious choice of observable functions to realize global linear representations of nonlinear systems in the lifted observable space. However, real-world data is often noisy, making it difficult to obtain an accurate and unbiased approximation of the Koopman operator. The Koopman operator generated from noisy
datasets is typically corrupted by noise-induced bias that severely degrades prediction and downstream tracking performance. In order to address this drawback, this paper proposes a novel autoencoder-based neural architecture to jointly learn the appropriate lifting functions and the reduced-bias Koopman operator from noisy data. The architecture initially learns the Koopman basis functions that are consistent for both the forward and backward temporal dynamics of the system. Subsequently, by utilizing the learned forward and backward temporal dynamics, the Koopman operator is synthesized with a reduced bias making the method more robust to noise compared to existing techniques. Theoretical analysis is used to demonstrate significant bias reduction in the presence of training noise. Dynamics prediction and tracking control simulations are conducted for multiple serial manipulator arms, including performance comparisons with leading alternative designs, to demonstrate its robustness under various noise levels. Experimental studies with the Franka FR3 7-DoF manipulator arm are further used to demonstrate the effectiveness of the proposed approach in a practical setting.
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Robotics
What you reward is what you learn: Comparing rewards for online speech policy optimization in public HRI
Sichao Song, Yuki Okafuji, Kaito Ariu et al. (+1 more)
Designing policies that are both efficient and acceptable for conversational service
robots in open and diverse environments is non-trivial. Unlike fixed, hand-tuned parameters, online learning can adapt to non-stationary conditions. In this paper, we study how to adapt a social
robot's
speech policy in the wild. During a 12-day in-situ deployment with over 1,400 public encounters, we cast online policy optimization as a multi-armed bandit problem and use Thompson sampling to select among six actions defined by
speech rate (slow/normal/fast) and verbosity (concise/detailed). We compare three complementary binary rewards–Ru (user rating), Rc (conversation closure), and Rt (>=2 turns)–and show that each induces distinct arm distributions and interaction behaviors. We complement the online results with offline evaluations that analyze contextual factors (e.g., crowd level, group size) using
video-annotated data. Taken together, we distill ready-to-use design lessons for deploying online optimization of
speech policies in real public HRI settings.
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All authors Sichao Song, Yuki Okafuji, Kaito Ariu, Amy Koike
Robotics
Learning Diffusion Policy from Primitive Skills for Robot Manipulation
Zhihao Gu, Ming Yang, Difan Zou et al. (+1 more)
Diffusion policies (DP) have recently shown great promise for generating actions in robotic manipulation. However, existing approaches often rely on global instructions to produce short-term control signals, which can result in misalignment in action generation. We
conjecture that the primitive skills, referred to as fine-grained, short-horizon manipulations, such as “move up” and “open the gripper”, provide a more intuitive and effective interface for
robot learning. To bridge this gap, we propose SDP, a skill-conditioned DP that integrates interpretable skill learning with conditional action planning. SDP abstracts eight reusable primitive skills across tasks and employs a
vision-
language model to extract discrete representations from visual observations and
language instructions. Based on them, a lightweight router network is designed to assign a desired primitive skill for each state, which helps construct a single-skill policy to generate skill-aligned actions. By decomposing complex tasks into a sequence of primitive skills and selecting a single-skill policy, SDP ensures skill-consistent behavior across diverse tasks. Extensive experiments on two challenging simulation
benchmarks and real-world
robot deployments demonstrate that SDP consistently outperforms SOTA methods, providing a new paradigm for skill-based
robot learning with
diffusion policies.
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All authors Zhihao Gu, Ming Yang, Difan Zou, Dong Xu
Robotics
From Metrics to Meaning: Insights from a Mixed-Methods Field Experiment on Retail Robot Deployment
Sichao Song, Yuki Okafuji, Takuya Iwamoto et al. (+2 more)
We report a mixed-methods field experiment of a conversational service
robot deployed under everyday staffing discretion in a live bedding store. Over 12 days we alternated three conditions–Baseline (no
robot),
Robot-only, and
Robot+Fixture–and
video-annotated the service funnel from passersby to purchase. An explanatory sequential design then used six post-experiment staff interviews to interpret the quantitative patterns. Quantitatively, the
robot increased stopping per passerby (highest with the fixture), yet clerk-led downstream steps per stopper–clerk approach, store entry, assisted experience, and purchase–decreased. Interviews explained this divergence: clerks avoided interrupting ongoing
robot-customer talk, struggled with ambiguous timing amid conversational latency, and noted child-centered attraction that often satisfied curiosity at the doorway. The fixture amplified visibility but also anchored encounters at the threshold, creating a well-defined micro-space where needs could “close” without moving inside. We synthesize these strands into an integrative account from the initial show of interest on the part of a customer to their entering the store and derive actionable guidance. The results advance the understanding of interactions between customers, staff members, and the
robot and offer practical recommendations for deploying service
robots in high-touch retail.
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All authors Sichao Song, Yuki Okafuji, Takuya Iwamoto, Jun Baba, Hiroshi Ishiguro
Robotics
CausalNav: A Long-term Embodied Navigation System for Autonomous Mobile Robots in Dynamic Outdoor Scenarios
Hongbo Duan, Shangyi Luo, Zhiyuan Deng et al. (+5 more)
Autonomous
language-guided
navigation in large-scale outdoor environments remains a key challenge in mobile robotics, due to difficulties in semantic
reasoning, dynamic conditions, and long-term stability. We propose CausalNav, the first scene graph-based semantic
navigation framework tailored for dynamic outdoor environments. We construct a multi-level semantic scene graph using LLMs, referred to as the
Embodied Graph, that hierarchically integrates coarse-grained map data with fine-grained object entities. The constructed graph serves as a retrievable knowledge base for
Retrieval-Augmented Generation (RAG), enabling semantic
navigation and long-range planning under open-vocabulary queries. By fusing real-time perception with offline map data, the
Embodied Graph supports robust
navigation across varying spatial granularities in dynamic outdoor environments. Dynamic objects are explicitly handled in both the scene graph construction and hierarchical planning modules. The
Embodied Graph is continuously updated within a temporal window to reflect environmental changes and support real-time semantic
navigation. Extensive experiments in both simulation and real-world settings demonstrate superior robustness and efficiency.
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All authors Hongbo Duan, Shangyi Luo, Zhiyuan Deng, Yanbo Chen, Yuanhao Chiang, Yi Liu, Fangming Liu, Xueqian Wang
Robotics
DisCo-FLoc: Semantic-Free Floorplan Localization via $SE(2)$-Aware Contrastive Disambiguation
Ping Zhong, Shiyong Meng, Bolei Chen et al. (+3 more)
Visual Floorplan Localization (FLoc) struggles with severe structural aliasing caused by repetitive minimalist layouts. This occurs because physically distant poses share highly similar visual-
geometric features, which degrades spatial separability and angular discriminability. While existing methods attempt to mitigate these ambiguities by relying on costly semantic annotations, the resulting performance gains remain inherently limited. To address the above issues, we propose DisCo-FLoc, a semantic-free method for visual-
geometric Contrastive Disambiguation. First, we introduce a depth-aware Ray Regression Predictor (RRP) that serves as a dense-to-ray
geometric projector. By explicitly suppressing visual clutter along the vertical dimension, RRP projects
monocular RGB images into 2D ray primitives, which are matched with floorplans to produce geometry-aware FLoc candidates. Second, to resolve the remaining ambiguity among these candidates, we propose a spatially perturbed contrastive objective to align RGB images with local floorplan structures and formulate a visual-
geometric compatibility function. In particular, we meticulously construct positive and negative samples at both positional and directional levels through $SE(2)$ pose perturbations for contrastive learning, effectively achieving pose smoothness, spatial separability, and angular discriminability. The compatibility function enables DisCo-FLoc to disambiguate FLoc by using richer visual context beyond pure
geometric layouts, without requiring any semantic annotations. Extensive experiments on two challenging visual FLoc
benchmarks demonstrate that DisCo-FLoc significantly outperforms state-of-the-art semantic-based methods, especially narrowing the performance gap between positional and directional FLoc accuracy.
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All authors Ping Zhong, Shiyong Meng, Bolei Chen, Tao Zou, Chaoxu Mu, Jianxin Wang
Robotics
AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
Yanhao Wu, Haoyang Zhang, Fei He et al. (+6 more)
Practical autonomous driving requires models that generalize by
reasoning through spatial-temporal possibilities to exclude unsafe outcomes. While state-of-the-art (SOTA) methods use parallel planning architectures, they fail to explicitly couple speed decisions with
agent behavior along the driving path, leading to suboptimal coordination. To address this, we propose a cascaded framework that transforms longitudinal planning from an independent prediction task into a path-conditioned
reasoning process. On the model side, we introduce an anchor-based regression design that conditions longitudinal prediction on the lateral drive path, and reformulate longitudinal planning as 1D displacement prediction along the path. This reduces
geometric uncertainty and sharpens the model's focus on interaction-driven dynamics. On the data side, we introduce a planning-oriented data augmentation strategy that simulates rare safety-critical events by programmatically inserting
agents and relabeling longitudinal targets to enforce collision avoidance. Evaluated on the challenging Bench2Drive
benchmark, our method achieves SOTA performance with a driving score of 89.07 and a success rate of 73.18%, demonstrating significantly improved coordination and safety. Further evaluation on Fail2Drive confirms strong generalization to rare edge cases where parallel formulations typically fail. Project page:https://yanhaowu.github.io/AlignDrive/.
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All authors Yanhao Wu, Haoyang Zhang, Fei He, Rui Wu, Yanhu Shan, Congpei Qiu, Liang Gao, Wei Ke, Tong Zhang