Robotics
SwiftBot: A Decentralized Platform for LLM-Powered Federated Robotic Task Execution
YueMing Zhang, Shuai Xu, Zhengxiong Li et al. (+3 more)
Federated robotic task execution systems require bridging natural
language instructions to distributed
robot control while efficiently managing computational resources across heterogeneous edge devices without centralized coordination. Existing approaches face three limitations: rigid hand-coded planners requiring extensive domain engineering, centralized coordination that contradicts
federated collaboration as
robots scale, and static resource allocation failing to share containers across
robots when workloads shift dynamically. We present SwiftBot, a
federated task execution platform that integrates LLM-based task decomposition with intelligent container orchestration over a DHT overlay, enabling
robots to collaboratively execute tasks without centralized control. SwiftBot achieves 94.3% decomposition accuracy across diverse tasks, reduces task startup latency by 1.5-5.4x and average training latency by 1.4-2.5x, and improves tail latency by 1.2-4.7x under high load through
federated warm container migration. Evaluation on multimedia tasks validates that co-designing semantic understanding and
federated resource management enables both flexibility and efficiency for robotic task control.
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All authors YueMing Zhang, Shuai Xu, Zhengxiong Li, Fangtian Zhong, Xiaokun Yang, Hailu Xu
Robotics
Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving
Chen Xiong, Ziwen Wang, Deqi Wang et al. (+4 more)
Autonomous driving testing increasingly relies on mining safety critical scenarios from large scale naturalistic driving data, yet existing screening pipelines still depend on manual risk annotation and expensive frame by frame risk evaluation, resulting in low efficiency and weakly grounded risk quantification. To address this issue, we propose a driver risk fusion based hazardous scenario screening method for autonomous driving. During training, the method combines an improved Driver Risk Field with a dynamic cost model to generate high quality risk supervision signals, while during inference it directly predicts scenario level risk scores through fast forward passes, avoiding per frame risk computation and enabling efficient large scale ranking and
retrieval. The improved Driver Risk Field introduces a new risk height function and a speed adaptive look ahead mechanism, and the dynamic cost model integrates kinetic energy, oriented bounding box constraints, and Gaussian kernel
diffusion smoothing for more accurate interaction modeling. We further design a risk trajectory cross
attention decoder to jointly decode risk and trajectories. Experiments on the INTERACTION and FLUID
datasets show that the proposed method produces smoother and more discriminative risk estimates. On FLUID, it achieves an AUC of 0.792 and an AP of 0.825, outperforming PODAR by 9.1 percent and 5.1 percent, respectively, demonstrating its effectiveness for scalable risk labeling and hazardous scenario screening.
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All authors Chen Xiong, Ziwen Wang, Deqi Wang, Cheng Wang, Yiyang Chen, He Zhang, Chao Gou
Robotics
A Distributed Gaussian Process Model for Multi-Robot Mapping
Seth Nabarro, Mark van der Wilk and Andrew J. Davison
We propose DistGP: a multi-
robot learning method for collaborative learning of a global function using only local experience and computation. We utilise a sparse Gaussian process (GP) model with a factorisation that mirrors the multi-
robot structure of the task, and admits distributed training via Gaussian belief propagation (GBP). Our loopy model outperforms Tree-Structured GPs \cite{bui2014tree} and can be trained online and in settings with dynamic connectivity. We show that such distributed, asynchronous training can reach the same performance as a centralised, batch-trained model, albeit with slower convergence. Last, we compare to DiNNO \cite{yu2022dinno}, a distributed neural network (NN) optimiser, and find DistGP achieves superior accuracy, is more robust to sparse communication and is better able to learn continually.
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Robotics · Cross-listed from cs.CV
A Lightweight Digital-Twin-Based Framework for Edge-Assisted Vehicle Tracking and Collision Prediction
Murat Arda Onsu, Poonam Lohan, Burak Kantarci et al. (+3 more)
Vehicle tracking, motion estimation, and collision prediction are fundamental components of traffic safety and management in Intelligent Transportation Systems (ITS). Many recent approaches rely on computationally intensive prediction models, which limits their practical deployment on resource-constrained edge devices. This paper presents a lightweight digital-twin-based framework for vehicle tracking and spatiotemporal collision prediction that relies solely on object detection, without requiring complex trajectory prediction networks. The framework is implemented and evaluated in Quanser Interactive Labs (QLabs), a high-fidelity digital twin of an urban traffic environment that enables controlled and repeatable scenario generation. A YOLO-based detector is deployed on simulated edge cameras to localize vehicles and extract frame-level centroid trajectories. Offline path maps are constructed from multiple traversals and indexed using K-D trees to support efficient online association between detected vehicles and road segments. During runtime, consistent vehicle identifiers are maintained, vehicle speed and direction are estimated from the temporal
evolution of path indices, and future positions are predicted accordingly. Potential collisions are identified by analyzing both spatial proximity and temporal overlap of predicted future trajectories. Our experimental results across diverse simulated urban scenarios show that the proposed framework predicts approximately 88% of collision events prior to occurrence while maintaining low computational overhead suitable for edge deployment. Rather than introducing a computationally intensive prediction model, this work introduces a lightweight digital-twin-based solution for vehicle tracking and collision prediction, tailored for real-time edge deployment in ITS.
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All authors Murat Arda Onsu, Poonam Lohan, Burak Kantarci, Aisha Syed, Matthew Andrews, Sean Kennedy
Robotics · Cross-listed from cs.CV
Faster-HEAL: An Efficient and Privacy-Preserving Collaborative Perception Framework for Heterogeneous Autonomous Vehicles
Armin Maleki and Hayder Radha
Collaborative perception (CP) is a promising paradigm for improving situational awareness in autonomous vehicles by overcoming the limitations of single-
agent perception. However, most existing approaches assume homogeneous
agents, which restricts their applicability in real-world scenarios where vehicles use diverse sensors and perception models. This heterogeneity introduces a feature domain gap that degrades detection performance. Prior works address this issue by retraining entire models/major components, or using feature interpreters for each new
agent type, which is computationally expensive, compromises
privacy, and may reduce single-
agent accuracy. We propose Faster-HEAL, a lightweight and
privacy-preserving CP framework that fine-tunes a low-rank visual
prompt to align heterogeneous features with a unified feature space while leveraging pyramid fusion for robust feature aggregation. This approach reduces the trainable parameters by 94%, enabling efficient adaptation to new
agents without retraining large models. Experiments on the OPV2V-H
dataset show that Faster-HEAL improves detection performance by 2% over state-of-the-art methods with significantly lower computational overhead, offering a practical solution for scalable heterogeneous CP.
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Robotics
Soft Rigid Hybrid Gripper with Inflatable Silicone Pockets for Tunable Frictional Grasping
Hoang Hiep Ly, Cong-Nhat Nguyen, Doan-Quang Tran et al. (+6 more)
Grasping objects with diverse mechanical properties, such as heavy, slippery, or fragile items, remains a significant challenge in robotics. Conventional rigid grippers typically rely on increasing the normal forces to secure an object, however, this can cause damage to fragile objects due to excessive force. To address this limitation, we propose a soft rigid hybrid gripper finger that combines rigid structural shells with soft, inflatable silicone pockets, which could be integrated into a conventional gripper. The hybrid gripper can actively modulate its surface friction by varying the internal air pressure of the silicone pockets, enabling the gripper to securely grasp objects without increasing the gripping force. This is demonstrated by fundamental experimental results, in which an increase in internal pressure leads to a proportional increase in the effective coefficient of friction. The gripping experiments also show that the integrated gripper can stably lift heavy and slippery objects or fragile, deformable objects, such as eggs, tofu, fruits, and paper cups, with minimal damage by increasing friction rather than applying high force.
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All authors Hoang Hiep Ly, Cong-Nhat Nguyen, Doan-Quang Tran, Quoc-Khanh Dang, Ngoc Duy Tran, Thi Thoa Mac, Anh Nguyen, Xuan-Thuan Nguyen, Tung D. Ta
Robotics
Kinematics-Aware Latent World Models for Data-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu et al. (+1 more)
Data-efficient learning remains a central challenge in autonomous driving due to the high cost and safety risks of large-scale real-world interaction. Although world-model-based reinforcement learning enables policy optimization through latent imagination, existing approaches often lack explicit mechanisms to encode spatial and kinematic structure essential for driving tasks. In this work, we build upon the Recurrent State-Space Model (RSSM) and propose a kinematics-aware latent world model framework for autonomous driving. Vehicle kinematic information is incorporated into the observation encoder to ground latent transitions in physically meaningful motion dynamics, while geometry-aware supervision regularizes the RSSM latent state to capture task-relevant spatial structure beyond pixel reconstruction. The resulting structured latent dynamics improve long-horizon imagination fidelity and stabilize policy optimization. Experiments in a driving simulation
benchmark demonstrate consistent gains over both model-free and pixel-based world-model baselines in terms of sample efficiency and driving performance. Ablation studies further verify that the proposed design enhances spatial representation quality within the latent space. These results suggest that integrating kinematic grounding into RSSM-based world models provides a scalable and physically grounded paradigm for autonomous driving policy learning.
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All authors Jiazhuo Li, Linjiang Cao, Qi Liu, Xi Xiong
Robotics
Vision-Guided MPPI for Agile Drone Racing: Navigating Arbitrary Gate Poses via Neural Signed Distance Fields
Fangguo Zhao, Hanbing Zhang, Zhouheng Li et al. (+2 more)
Autonomous drone racing requires the tight coupling of perception, planning, and control under extreme agility. However, recent approaches typically rely on precomputed spatial reference trajectories or explicit 6-DoF gate pose estimation, rendering them brittle to spatial perturbations, unmodeled track changes, and sensor noise. Conversely, end-to-end learning policies frequently overfit to specific track layouts and struggle with zero-shot generalization. To address these fundamental limitations, we propose a fully onboard,
vision guided optimal control framework that enables reference-free agile flight through arbitrarily placed and oriented gates. Central to our approach is Gate-SDF, a novel, implicitly learned neural signed distance field. Gate-SDF directly processes raw, noisy depth images to predict a continuous spatial field that provides both collision repulsion and active
geometric guidance toward the valid traversal area. We seamlessly integrate this representation into a sampling-based Model Predictive Path Integral (MPPI) controller. By fully exploiting GPU parallelism, the framework evaluates these continuous spatial constraints across thousands of simulated trajectory rollouts simultaneously in real time. Furthermore, our formulation inherently maintains spatial consistency, ensuring robust
navigation even under severe visual occlusion during aggressive maneuvers. Extensive simulations and real-world experiments demonstrate that the proposed system achieves high-speed agile flight and successfully navigates unseen tracks subject to severe unmodeled gate displacements and orientation perturbations.
Videos are available at https://zhaofangguo.github.io/
vision_guided_mppi/
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All authors Fangguo Zhao, Hanbing Zhang, Zhouheng Li, Xin Guan, Shuo Li
Robotics
RoTri-Diff: A Spatial Robot-Object Triadic Interaction-Guided Diffusion Model for Bimanual Manipulation
Zixuan Chen, Nga Teng Chan, Yiwen Hou et al. (+8 more)
Bimanual manipulation is a fundamental robotic skill that requires continuous and precise coordination between two arms. While imitation learning (IL) is the dominant paradigm for acquiring this capability, existing approaches, whether
robot-centric or object-centric, often overlook the dynamic
geometric relationship among the two arms and the manipulated object. This limitation frequently leads to inter-arm collisions, unstable grasps, and degraded performance in complex tasks. To address this, in this paper we explicitly models the
Robot-Object Triadic Interaction (RoTri) representation in bimanual systems, by encoding the relative 6D poses between the two arms and the object to capture their spatial triadic relationship and establish continuous triangular
geometric constraints. Building on this, we further introduce RoTri-Diff, a
diffusion-based imitation learning framework that combines RoTri constraints with
robot keyposes and object motion in a hierarchical
diffusion process. This enables the generation of stable, coordinated trajectories and robust execution across different modes of bimanual manipulation. Extensive experiments show that our approach outperforms state-of-the-art baselines by 10.2% on 11 representative RLBench2 tasks and achieves stable performance on 4 challenging real-world bimanual tasks. Project website: https://rotri-diff.github.io/.
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All authors Zixuan Chen, Nga Teng Chan, Yiwen Hou, Chenrui Tie, Zixuan Liu, Haonan Chen, Junting Chen, Jieqi Shi, Yang Gao, Jing Huo, Lin Shao
Robotics
Tutorial on Aided Inertial Navigation Systems: A Modern Treatment Using Lie-Group Theoretical Methods
Soulaimane Berkane
Robots and autonomous vehicles rely on accurate estimates of their position, velocity, and orientation. Inertial sensors measure motion at high rates, but errors accumulate as these measurements are integrated. Satellite
navigation, cameras, and other sensors help limit this drift. Combining these sources effectively requires estimation methods that account for the geometry of the
navigation state. This tutorial presents a unified introduction to aided inertial
navigation from a Lie-group perspective. It connects motion prediction, uncertainty propagation, and measurement updates within a common
geometric framework. Particular
attention is given to invariant filtering, which uses system
symmetries to simplify the description and
evolution of estimation errors. The tutorial explains how choices of state and error representations shape the filter equations. It connects theoretical principles with formulas suitable for implementation and discusses recent extensions addressing convergence and sensor biases. By organizing established and emerging methods around their shared
geometric foundations, the presentation provides students, researchers, and engineers with a basis for understanding, implementing, and extending
navigation algorithms.
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Robotics
Model-based thermal drift compensation for high-precision hexapod robot actuators
Clément Robert, Alain Vissiere, Olivier Company et al. (+3 more)
Thermal expansion is a significant source of positioning error in high-precision hexapod
robots (Gough-Stewart platforms). Any variation in the temperature of the hexapod's parts induces expansion, which alters their kinematic model and reduces the
robot's accuracy and repeatability. These variations may arise from internal heat sources (such as motors, encoders, and electronics) or from environmental changes. In this study, a method is proposed to anticipate and therefore correct the thermal drift of one of the hexapod precision electro-mechanical actuators. This method is based on determining a model that links the expansion state of the actuator at any given moment to the temperature of some well-chosen points on its surface. This model was initially developed theoretically. Its coefficients were then adjusted experimentally on a specific test-bench, based on a rigorous measurement campaign of actuator expansion using a high-precision interferometric measurement system. Experimental validation demonstrates a reduction of thermally induced expansion by more than 80%. This paves the way for thermal drift correction across the entire
robot or similar robotics parts.
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All authors Clément Robert, Alain Vissiere, Olivier Company, Pierre Noire, Thierry Roux, Sébastien Krut
Robotics
DexKnot: Generalizable Visuomotor Policy Learning for Dexterous Bag-Knotting Manipulation
Jiayuan Zhang, Ruihai Wu, Haojun Chen et al. (+5 more)
Knotting plastic bags is a common task in daily life, yet it is challenging for
robots due to the bags' infinite degrees of freedom and complex physical dynamics. Existing methods often struggle in generalization to unseen bag instances or deformations. To address this, we present DexKnot, a framework that combines keypoint affordance with
diffusion policy to learn a generalizable bag-knotting policy. Our approach learns a shape-agnostic representation of bags from keypoint correspondence data collected through real-world manual deformation. For an unseen bag configuration, the keypoints can be identified by matching the representation to a reference. These keypoints are then provided to a
diffusion transformer, which generates
robot action based on a small number of human demonstrations. DexKnot enables effective policy generalization by reducing the dimensionality of observation space into a sparse set of keypoints. Experiments show that DexKnot achieves reliable and consistent knotting performance across a variety of previously unseen instances and deformations.
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All authors Jiayuan Zhang, Ruihai Wu, Haojun Chen, Yuran Wang, Yifan Zhong, Ceyao Zhang, Yaodong Yang, Yuanpei Chen
Robotics
Efficient Trajectory Optimization for Autonomous Racing via Formula-1 Data-Driven Initialization
Samir Shehadeh, Lukas Kutsch, Nils Dengler et al. (+2 more)
Trajectory optimization is a central component of fast and efficient autonomous racing. However practical optimization pipelines remain highly sensitive to initialization and may converge slowly or to suboptimal local solutions when seeded with heuristic trajectories such as the centerline or minimum-
curvature paths. To address this limitation, we leverage expert driving behavior as a initialization prior and propose a learning-informed initialization strategy based on real-world Formula~1 telemetry. To this end, we first construct a multi-track Formula~1 trajectory
dataset by reconstructing and aligning noisy GPS telemetry to a standardized reference-line representation across 17 tracks. Building on this, we present a neural network that predicts an expert-like raceline offset directly from local track geometry, without explicitly modeling vehicle dynamics or forces. The predicted raceline is then used as an informed seed for a minimum-time optimal control solver. Experiments on all 17 tracks demonstrate that the learned initialization accelerates solver convergence and significantly reduces runtime compared to traditional
geometric baselines, while preserving the final optimized lap time.
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All authors Samir Shehadeh, Lukas Kutsch, Nils Dengler, Sicong Pan, Maren Bennewitz
Robotics
Learning From Failures: Efficient Reinforcement Learning Control with Episodic Memory
Chenyang Miao
Reinforcement learning has achieved remarkable success in
robot learning. However, under challenging exploration and contact-rich dynamics, early-stage training is frequently dominated by premature terminations such as collisions and falls. As a result, learning is overwhelmed by short-horizon, low-return trajectories, which hinder convergence and limit long-horizon exploration. To alleviate this issue, we propose a technique called Failure Episodic
Memory Alert (FEMA). FEMA explicitly stores short-horizon failure experiences through an episodic
memory module. During interactions, it retrieves similar failure experiences and prevents the
robot from recurrently relapsing into unstable states, guiding the policy toward long-horizon trajectories with greater long-term value. FEMA can be combined easily with model-free reinforcement learning
algorithms, and yields a substantial sample-efficiency improvement of 33.11% on MuJoCo tasks across several classical RL
algorithms. Furthermore, integrating FEMA into a parallelized PPO training pipeline demonstrates its effectiveness on a real-world bipedal
robot task.
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Robotics
Structured Multitask Gaussian Processes for Probabilistic Full-Body Human Motion Prediction
Jinger Chong, Xiaotong Zhang and Kamal Youcef-Toumi
Robots that share space with people need human motion predictions with reliable uncertainty, not just accurate means. This paper presents a structured multitask variational Gaussian process (GP) for probabilistic full-body human motion prediction. The model integrates one-shot prediction of the full horizon, multitask outputs that couple future time steps, a factorization into one sparse variational GP per joint-dimension pair of the rotation representation, and a continuous 6D rotation representation with forward kinematics that preserves bone lengths. Its predictive distribution is available in closed form: a Gaussian with full temporal covariance for each rotation coordinate, which downstream planners operating in rotation coordinates can use without sampling, density estimation, or forward kinematics. On Human3.6M, the model achieves lower kernel-density-estimated negative log-likelihood (KDE NLL) than Motron and DLow at every prediction step, 22-52 nats below Motron. Empirical coverage is conservative at the 50% and 80% levels and close to nominal at 95% over a 2 s horizon. The probabilistic model uses 0.24 M parameters, about seven times fewer than Motron, and a separately trained 0.35 M-parameter deterministic variant has a mean angle error 3-22% higher than Motron's. Ablations confirm the benefit of the 6D representation and show that, at a fixed training budget, factorized joint outputs outperform coupled right-arm alternatives in NLL and CRPS while using less
memory. These results show that compact, structured GPs provide analytic, temporally structured uncertainty with conservative empirical coverage for robotic systems.
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Robotics
ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation
Ziyi Zhou, Pengyuan Shu, Ruize Cao et al. (+2 more)
Collaborative transportation of heavy payloads via loco-manipulation is a challenging yet essential capability for legged
robots operating in complex, unstructured environments. Centralized planning methods, e.g., holistic trajectory optimization, capture dynamic coupling among
robots and payloads but scale poorly with system size, limiting real-time applicability. In contrast, hierarchical and fully decentralized approaches often neglect force and dynamic interactions, leading to conservative behavior. This study proposes an Alternating Direction Method of Multipliers (ADMM)-based distributed model predictive control framework for collaborative loco-manipulation with a team of quadruped
robots with manipulators. By exploiting the payload-induced coupling structure, the global optimal control problem is decomposed into parallel individual-
robot-level subproblems with consensus constraints. The distributed planner operates in a receding-horizon fashion and achieves fast convergence, requiring only a few ADMM iterations per planning cycle. A wrench-aware whole-body controller executes the planned trajectories, tracking both motion and interaction wrenches. Extensive simulations with up to four
robots demonstrate scalability, real-time performance, and robustness to model uncertainty.
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All authors Ziyi Zhou, Pengyuan Shu, Ruize Cao, Yuntian Zhao, Ye Zhao
Robotics
VLN-Cache: Enabling Token Caching for VLN Models with Visual/Semantic Dynamics Awareness
Zihao Zheng, Zhihao Mao, Xingyue Zhou et al. (+9 more)
Vision-and-
Language Navigation (VLN) increasingly relies on large
vision-
language models, but their inference cost conflicts with real-time deployment.
Token caching is a promising training-free strategy that avoids redundant computation by reusing stable visual
tokens across frames. However, existing methods assume a static camera and fixed semantic focus, assumptions that VLN fundamentally violates. We identify two failure modes: (1) visual dynamics, where viewpoint shift displaces
token positions across frames, causing position-wise matching to pair misaligned content; (2) semantic dynamics, where
token relevance shifts across task stages as
navigation progresses, making cached states stale. We propose VLN-Cache, a visual-dynamic-aware and semantic-dynamic-aware caching framework that introduces view-aligned remapping to recover
geometric correspondences and a task-relevance saliency filter to veto reuse at semantic transitions. A layer-adaptive
entropy policy further balances the per-layer reuse budget. Experiments on the R2R-CE simulation
benchmark show up to 1.52x speedup while maintaining competitive
navigation success rates.
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All authors Zihao Zheng, Zhihao Mao, Xingyue Zhou, Jiayu Chen, Maoliang Li, Xinhao Sun, Hailong Zou, Zhaobo Zhang, Xuanzhe Liu, Donggang Cao, Hong Mei, Xiang Chen
Robotics
The Talking Robot: Distortion-Robust Acoustic Models for Robot-Robot Communication
Hanlong Li, Karishma Kamalahasan, Jiahui Li et al. (+2 more)
We present Artoo, a learned acoustic communication system for
robots that replaces hand-designed signal processing with end-to-end co-trained neural networks. Our system pairs a lightweight text-to-
speech (TTS) transmitter (1.18M parameters) with a conformer-based automatic
speech recognition (ASR) receiver (938K parameters), jointly optimized through a differentiable channel. Unlike human
speech,
robot-to-
robot communication is paralinguistics-free: the system need not preserve timbre, prosody, or naturalness, only maximize decoding accuracy under channel distortion. Through a three-phase co-training curriculum, the TTS transmitter learns to produce distortion-robust acoustic encodings that surpass the baseline under noise, achieving 8.3% CER at 0 dB SNR. The entire system requires only 2.1M parameters (8.4 MB) and runs in under 13 ms end-to-end on a CPU, making it suitable for deployment on resource-constrained robotic platforms.
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All authors Hanlong Li, Karishma Kamalahasan, Jiahui Li, Kazuhiro Nakadai, Shreyas Kousik
Robotics
Morphology-Independent Facial Expression Imitation for Human-Face Robots
Xu Chen, Rui Gao, Che Sun et al. (+4 more)
Accurate facial expression imitation on human-face
robots is crucial for achieving natural human-
robot interaction. Most existing methods have achieved photorealistic expression imitation through mapping 2D facial landmarks to a
robot's actuator commands. Their imitation of landmark trajectories is susceptible to interference from facial morphology, which would lead to a performance drop. In this paper, we propose a morphology-independent expression imitation method that decouples expressions from facial morphology to eliminate morphological influence and produce more realistic expressions for human-face
robots. Specifically, we construct an expression decoupling module to learn expression semantics by disentangling the expression representation from the morphology representation in a self-supervised manner. We devise an expression transfer module to map the representations to the
robot's actuator commands through a learning objective of perceiving expression errors, producing accurate facial expressions based on the learned expression semantics. To support experimental validation, a custom-designed and highly expressive human-face
robot, namely Pengrui, is developed to serve as an experimental platform for realistic expression imitation. Extensive experiments demonstrate that our method enables the human-face
robot to reproduce a wide range of human-like expressions effectively. All code and implementation details of the
robot will be released.
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All authors Xu Chen, Rui Gao, Che Sun, Zhehang Liu, Yuwei Wu, Shuo Yang, Yunde Jia
Robotics
GuideTWSI: A Diverse Tactile Walking Surface Indicator Dataset from Synthetic and Real-World Images for Blind and Low-Vision Navigation
Hochul Hwang, Soowan Yang, Anh N. H. Nguyen et al. (+6 more)
Tactile Walking Surface Indicators (TWSIs) are safety-critical landmarks that blind and low-
vision (BLV) pedestrians use to locate crossings and hazard zones. From our observation sessions with BLV guide dog handlers, trainers, and an O&M specialist, we confirmed the critical importance of reliable and accurate TWSI
segmentation for
navigation assistance of BLV individuals. Achieving such reliability requires large-scale annotated data. However, TWSIs are severely underrepresented in existing urban perception
datasets, and even existing dedicated paving
datasets are limited: they lack
robot-relevant viewpoints (e.g.,
egocentric or top-down) and are geographically biased toward East Asian directional bars - raised parallel strips used for continuous guidance along sidewalks. This narrow focus overlooks truncated domes - rows of round bumps used primarily in North America and Europe as detectable warnings at curbs, crossings, and platform edges. As a result, models trained only on bar-centric data struggle to generalize to dome-based warnings, leading to missed detections and false stops in safety-critical environments.
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All authors Hochul Hwang, Soowan Yang, Anh N. H. Nguyen, Parth Goel, Krisha Adhikari, Sunghoon I. Lee, Joydeep Biswas, Nicholas A. Giudice, Donghyun Kim
Robotics
TacDexGrasp: Compliant and Robust Dexterous Grasping with Tactile Feedback
Yubin Ke, Jiayi Chen, Hang Lv et al. (+2 more)
Multi-fingered hands offer great potential for compliant and robust grasping of unknown objects, yet their high-dimensional force control presents a significant challenge. This work addresses two key problems: (1) distributing forces across multiple contacts to counteract an object's weight, and (2) preventing rotational slip caused by gravitational torque when a grasp is distant from the object's center of mass. We address these challenges via tactile feedback and a Second-Order Cone Programming (SOCP)-based controller, without explicit torque modeling or slip detection. Our key insights are (1) rotational slip inevitably induces translational slip at some contact points for a multi-fingered grasp, and (2) the ratio of tangential to normal force at each contact is an effective early stability indicator. By actively constraining this ratio for each finger below the estimated friction coefficient, our controller maintains grasp stability against both translational and rotational slip. Real-world experiments on 12 diverse objects demonstrate the robustness and compliance of our approach.
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All authors Yubin Ke, Jiayi Chen, Hang Lv, Xiao Zhou, He Wang