Author name cluster

Yao Su 0001

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

1 author row

IROS Conference 2025 Conference Paper

R-Tac0: A Rounded High-Frequency Transferable Monochrome Vision-based Tactile Sensor for Shape Reconstruction

Wanlin Li
Pei Lin
Meng Wang 0051
Chenxi Xiao
Kaspar Althoefer
Yao Su 0001
Ziyuan Jiao
Hangxin Liu

Endowing the curved surfaces of rounded vision-based tactile fingers is essential for dexterous robotic manipulation, as they offer more sufficient contact with the environment. However, current rounded designs are constrained by a low sensing frequency (30–60 Hz) and the need for recalibration when adapting to new sensors due to the reliance on multi-channel captures, which hinders their performance in dynamic robotic tasks and large-scale deployment. In this work, we introduce R-Tac0, a low-cost rounded VBTS engineered for high-resolution and high-speed perception. The key innovation is a monochrome vision-based sensing principle: utilizing a black-and-white camera to capture the reflection properties of the compound rounded elastomer under monochromatic illumination. This single-channel imaging significantly reduces data volume and simplifies computational complexity, enabling 120 Hz tactile perception. A lightweight neural network can calibrate the sensor to achieve a depth reconstruction accuracy of 0. 169 mm per pixel, while exhibiting surprisingly good transferability to new sensors. In experiments, we demonstrate the advantages of R-Tac0’s rounded design by evaluating its performance under different contact angles, its high-frequency perception in slip detection, and its effectiveness in robotic dynamic pose estimation.

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ICRA Conference 2024 Conference Paper

ASPIRe: An Informative Trajectory Planner with Mutual Information Approximation for Target Search and Tracking

Kangjie Zhou
Pengying Wu
Yao Su 0001
Han Gao
Ji Ma 0007
Hangxin Liu
Chang Liu 0002

This paper proposes an informative trajectory planning approach, namely, adaptive particle filter tree with sigma point-based mutual information reward approximation (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view. We develop a novel sigma point-based approximation to accurately estimate mutual information (MI) for general, non-Gaussian distributions utilizing particle representation of the belief state, while simultaneously maintaining high computational efficiency. Building upon the MI approximation, we develop the Adaptive Particle Filter Tree (APFT) approach with MI as the reward, which features belief state tree nodes for informative trajectory planning in continuous state and measurement spaces. An adaptive criterion is proposed in APFT to adjust the planning horizon based on the expected information gain. Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation and outperforms benchmark methods in terms of both search efficiency and estimation accuracy.

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IROS Conference 2024 Conference Paper

Flight Structure Optimization of Modular Reconfigurable UAVs

Yao Su 0001
Ziyuan Jiao
Zeyu Zhang 0001
Jingwen Zhang
Hang Li
Meng Wang 0051
Hangxin Liu

This paper presents a Genetic Algorithm (GA) designed to reconfigure a large group of modular Unmanned Aerial Vehicles (UAVs), each with different weights and inertia parameters, into an over-actuated flight structure with improved dynamic properties. Previous research efforts either utilized expert knowledge to design flight structures for a specific task or relied on enumeration-based algorithms that required extensive computation to find an optimal one. However, both approaches encounter challenges in accommodating the heterogeneity among modules. Our GA addresses these challenges by incorporating the complexities of over-actuation and dynamic properties into its formulation. Additionally, we employ a tree representation and a vector representation to describe flight structures, facilitating efficient crossover operations and fitness evaluations within the GA framework, respectively. Using cubic modular quadcopters capable of functioning as omnidirectional thrust generators, we validate that the proposed approach can (i) adeptly identify suboptimal configurations ensuring both over-actuation and trajectory tracking accuracy and (ii) significantly reduce computational costs compared to traditional enumeration-based methods.

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IROS Conference 2024 Conference Paper

Large-scale Deployment of Vision-based Tactile Sensors on Multi-fingered Grippers

Meng Wang 0051
Wanlin Li
Hao Liang
Boren Li
Kaspar Althoefer
Yao Su 0001
Hangxin Liu

Vision-based Tactile Sensors (VBTSs) show significant promise in that they can leverage image measurements to provide high-spatial-resolution human-like performance. However, current VBTS designs, typically confined to the fingertips of robotic grippers, prove somewhat inadequate, as many grasping and manipulation tasks require multiple contact points with the object. With an end goal of enabling large-scale, multi-surface tactile sensing via VBTSs, our research (i) develops a synchronized image acquisition system with minimal latency, (ii) proposes a modularized VBTS design for easy integration into finger phalanges, and (iii) devises a zero-shot calibration approach to improve data efficiency in the simultaneous calibration of multiple VBTSs. In validating the system within a miniature 3-fingered robotic gripper equipped with 7 VBTSs we demonstrate improved tactile perception performance by covering the contact surfaces of both gripper fingers and palm. Additionally, we show that our VBTS design can be seamlessly integrated into various end-effector morphologies significantly reducing the data requirements for calibration.

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IROS Conference 2024 Conference Paper

Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planning

Yilue Qian
Peiyu Yu
Ying Nian Wu
Yao Su 0001
Wei Wang 0115
Lifeng Fan

Visual planning simulates how humans make decisions to achieve desired goals in the form of searching for visual causal transitions between an initial visual state and a final visual goal state. It has become increasingly important in egocentric vision with its advantages in guiding agents to perform daily tasks in complex environments. In this paper, we propose an interpretable and generalizable visual planning framework consisting of i) a novel Substitution-based Concept Learner (SCL) that abstracts visual inputs into disentangled concept representations, ii) symbol abstraction and reasoning that performs task planning via the learned symbols, and iii) a Visual Causal Transition model (ViCT) that grounds visual causal transitions to semantically similar real-world actions. Given an initial state, we perform goal-conditioned visual planning with a symbolic reasoning method fueled by the learned representations and causal transitions to reach the goal state. To verify the effectiveness of the proposed model, we collect a large-scale visual planning dataset based on AI2-THOR, dubbed as CCTP. Extensive experiments on this challenging dataset demonstrate the superior performance of our method in visual planning. Empirically, we show that our framework can generalize to unseen task trajectories, unseen object categories, and real-world data. Further details of this work are provided at https://fqyqc.github.io/ConTranPlan/.

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ICRA Conference 2024 Conference Paper

Real-time Dynamic-consistent Motion Planning for Over-actuated UAVs

Yao Su 0001
Jingwen Zhang
Ziyuan Jiao
Hang Li
Meng Wang 0051
Hangxin Liu

Existing motion planning approaches for over-actuated unmanned aerial vehicle (UAV) platforms can achieve online planning without considering dynamics. However, in many envisioned application areas such as aerial manipulation, payload delivery, and moving target tracking, it is critical to ensure dynamic consistency in the generated trajectory. The dynamics of these platforms introduce a high nonlinearity, leading to a substantial increase in computational burden. This paper presents an efficient method to plan motions that are consistent with the dynamics of over-actuated UAVs. With a hierarchical control structure, the dimension of the optimization problem is greatly reduced with synthesized wrench commands. Additionally, by exploring the dynamics of over-actuated UAVs, the complex planning process is decoupled into two simpler sub-problems. As a result, the proposed planner can be solved as two small quadratic programmings (QPs) and deployed in real-time. The computational efficiency and dynamic consistency of the proposed method are verified through both simulations and experiments, including comparison with other approaches and dynamic target tracking.

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IROS Conference 2023 Conference Paper

Aggregating Single-Wheeled Mobile Robots for Omnidirectional Movements

Meng Wang 0051
Yao Su 0001
Hang Li
Jiarui Li
Jixiang Liang
Hangxin Liu

This paper presents a novel modular robot system that can self-reconfigure to achieve omnidirectional movements for collaborative object transportation. Each robotic module is equipped with a steerable omni-wheel for navigation and is shaped as a regular icositetragon with a permanent magnet installed on each corner for stable docking. After aggregating multiple modules and forming a structure that can cage a target object, we have developed an optimization-based method to compute the distribution of all wheels' heading directions, which enables efficient omnidirectional movements of the structure. By implementing a hierarchical controller on our prototyped system in both simulation and experiment, we validated the trajectory tracking performance of an individual module and a team of six modules in multiple navigation and collaborative object transportation settings. The results demonstrate that the proposed system can maintain a stable caging formation and achieve smooth transportation, indicating the effectiveness of our hardware and locomotion designs.

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IROS Conference 2023 Conference Paper

Sequential Manipulation Planning for Over-Actuated Unmanned Aerial Manipulators

Yao Su 0001
Jiarui Li
Ziyuan Jiao
Meng Wang 0051
Chi Chu
Hang Li
Yixin Zhu 0001
Hangxin Liu

We investigate the sequential manipulation planning problem for unmanned aerial manipulators (UAMs). Unlike prior work that primarily focuses on one-step manipulation tasks, sequential manipulations require coordinated motions of a UAM's floating base, the manipulator, and the object being manipulated, entailing a unified kinematics and dynamics model for motion planning under designated constraints. By leveraging a virtual kinematic chain (VKC)-based motion planning framework that consolidates components' kinematics into one chain, the sequential manipulation task of a UAM can be planned as a whole, yielding more coordinated motions. Integrating the kinematics and dynamics models with a hierarchical control framework, we demonstrate, for the first time, an over-actuated UAM achieves a series of new sequential manipulation capabilities in both simulation and experiment.

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IROS Conference 2022 Conference Paper

Downwash-aware Control Allocation for Over-actuated UAV Platforms

Yao Su 0001
Chi Chu
Meng Wang 0051
Jiarui Li
Liu Yang
Yixin Zhu 0001
Hangxin Liu

Tracking position and orientation independently affords more agile maneuver for over-actuated multirotor Unmanned Aerial Vehicles (UAVs) while introducing undesired downwash effects; downwash flows generated by thrust generators may counteract others due to close proximity, which significantly threatens the stability of the platform. The complexity of modeling aerodynamic airflow challenges control algorithms from properly compensating for such a side effect. Leveraging the input redundancies in over-actuated UAVs, we tackle this issue with a novel control allocation framework that considers downwash effects and explores the entire allocation space for an optimal solution. This optimal solution avoids downwash effects while providing high thrust efficiency within the hardware constraints. To the best of our knowledge, ours is the first formal derivation to investigate the downwash effects on over-actuated UAVs. We verify our framework on different hardware configurations in both simulation and experiment.

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IROS Conference 2018 Conference Paper

Multi-Limbed Robot Vertical Two Wall Climbing Based on Static Indeterminacy Modeling and Feasibility Region Analysis

Xuan Lin
Hari Krishnan
Yao Su 0001
Dennis W. Hong

This paper presents a technique to model statically indeterminate forces based on stiffness matrices for multi-limbed climbing robots. Current wall climbing robots in literature overlook statically indeterminate forces, causing an incapability to estimate climbing failure under certain circumstances. Accounting for these forces, robot deformation can be approximated, paving the way for the proposed two-wall climbing approach. During a wall climb, two failure modes, slide and over-torque, are identified to compute feasible climbing region. A hexapod robot is used to verify the proposed technique by climbing between walls with pure friction end effectors.

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