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Bidan Huang

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12 papers

1 author row

ICRA Conference 2025 Conference Paper

A Data-Efficient Progressive Learning Framework for Robot Scooping Task

Shuai Wang 0007
Entang Wang
Bidan Huang
Chong Zhang
Wei Wang
Yu Zheng 0001

Robot scooping is a challenging and important task in robotic tool manipulation research due to the complex relationship between the robot, the tool, and target objects/environment. Taking into account different tools, different target objects and varying environments, the required scooping manipulation strategy usually varies greatly. Even considering a specific type of spoon, the question of how to obtain a policy model that requires less demonstration data but shows better generalization capabilities deserves further exploration. In this paper, we propose a progressive learning framework for general robot scooping tasks, which requires a limited number of demonstrations but shows promising generalization capability. We first learn a scooping policy via human demonstrations with a specific setup. We then use this as a pre-train model for reinforcement learning in a curriculum manner to achieve a scooping strategy that is generalizable to different task setups. Finally, we evaluate the capabilities of the policy with a series of experiments both in simulation and on a real robot.

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IROS Conference 2025 Conference Paper

Robotic Hand Tool Use with Contact-Based Demonstration: The Case of Cucumber Peeling

Lingzi Xie
Shuai Wang 0007
Jingxiang Chen
Bidan Huang
Yi Zhang
Sicheng Yang
Yuyuan Chen
Wang Wei Lee

Robotic hand tool use has garnered significant attention from robotics researchers, because it enhances dexterity beyond the limitations imposed by manipulators with fixed tool configurations and human-involved manual tool changes. Despite extensive research, current methodologies predominantly focus on imitating human hand trajectories, often neglecting the pivotal role of tool-environment interaction. This study addresses this gap by exploring the task of cucumber peeling as a case study to implement contact-based demonstration strategies in robotic tool use. Our approach concentrates on the subtle tool contact behaviors that manifest through contact dynamics. Specifically, we select appropriate tool stiffness for the peeling tasks, which is captured via a handheld teaching device equipped with optical tactile sensors. Subsequently, object-level stiffness control strategies are employed to emulate these behaviors using a three-fingered robotic hand. Experimental results from real-world cucumber peeling trials substantiate our methodology, illustrating that the robotic hand can adjust contact through finger movements, thereby achieving humanlike peeling efficiency without necessitating alterations to the tool structure. This study not only demonstrates the feasibility of sophisticated tool use by robotic hands, but also highlights the critical importance of integrating tactile feedback to refine interaction with the environment.

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ICRA Conference 2024 Conference Paper

Thermoformed electronic skins for conformal tactile sensor arrays

Peng Lu
Jiaming Liang
Bidan Huang
Sicheng Yang
Wang Wei Lee

Robots and prostheses are increasingly designed with curvilinear surfaces for functional, aesthetic, aerodynamic, and safety reasons. Electronic skins (e-skins) capable of sensing contact location and pressure across complex, non-developable surfaces are essential for empowering next-generation robots with tactile awareness. This will facilitate safe and natural human-machine interactions while enhancing object manipulation capabilities. Despite the evident advantages of conformal e-skins, current fabrication methods face significant challenges in realizing their full potential. In this paper, we introduce thermoforming as a technique to efficiently fabricate tactile sensitive e-skins that conform to curvilinear surfaces. The performance, repeatability and uniformity of the sensors are characterized in detail. We also present a custom calibration pipeline where accurate digital replicas of conformal e-skins are generated for use in simulations. Finally, we demonstrate the benefits of 3D e-skins in a tool manipulation task.

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IROS Conference 2023 Conference Paper

A Unified Trajectory Generation Algorithm for Dynamic Dexterous Manipulation

Cheng Zhou
Wentao Gao
Weifeng Lu
Yanbo Long
Sicheng Yang
Longfei Zhao
Bidan Huang
Yu Zheng 0001

This paper proposes a novel efficient multi-phase trajectory generation algorithm for dynamic dexterous manipulation tasks, such as throwing, catching, dynamic regrasping, and dynamic handover, which can be decomposed into multiple manipulation primitives, including sticking, rolling, approaching, separating, colliding, and grasping. Each manipulation primitive is formulate as a free-terminal optimal control problem (OCP), aimed at computing the optimal pose (position and orientation) trajectories of the object and the robot subject to the pose and force linkage constraints between them and the expected force maintenance at contact. A single-arm regrasping task and a dual-arm dynamic handover task are conducted to demonstrate the effectiveness of the proposed algorithm.

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IROS Conference 2022 Conference Paper

Multi-fingered Tactile Servoing for Grasping Adjustment under Partial Observation

Hanzhong Liu
Bidan Huang
Qiang Li 0001
Yu Zheng 0001
Yonggen Ling
Wang Wei Lee
Yi Liu 0068
Ya-Yen Tsai

Grasping of objects using multi-fingered robotic hands often fails due to small uncertainties in the hand motion control and the object's pose estimation. To tackle this problem, we propose a grasping adjustment strategy based on tactile seroving. Our technique employs feedback from a sensorized multi-fingered robotic hand to collaboratively servo the fingers and palm to achieve the desired grasp. We demonstrate the performance of our method through simulation and physical experiments by having a robot grasp different objects under conditions of variable uncertainty. The results show that our approach achieved a higher success rate and tolerated greater uncertainty than an open-looped grasp.

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IROS Conference 2022 Conference Paper

Optimal Nonprehensile Interception Strategy for Objects in Flight

Cheng Zhou
Yanbo Long
Ying Cao
Longfei Zhao
Bidan Huang
Yu Zheng 0001

Intercepting an object in flight through nonpre-hensile manipulation is a challenging problem, which is aimed at catching and stopping a flying object using little contacts without completely restraining its relative motion to the robot. This paper presents a two-stage optimal trajectory generation method to tackle this problem. At the pre-catching stage, optimal position and attitude trajectories of the robot's end-effector to approach the object are generated by a variational method. At the post-catching stage, the end-effector's trajectories are generated to optimally eliminate the translational and rotational motion of the object and a convex-MPC algorithm combined with admittance control is used to realize the trajectory tracking. A series of simulations and experiments have been conducted to verify the effectiveness of the proposed method.

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IROS Conference 2021 Conference Paper

Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory

Zihan Ding
Ya-Yen Tsai
Wang Wei Lee
Bidan Huang

Reinforcement Learning (RL) methods have been widely applied for robotic manipulations via sim-to-real transfer, typically with proprioceptive and visual information. However, the incorporation of tactile sensing into RL for contact-rich tasks lacks investigation. In this paper, we model a tactile sensor in simulation and study the effects of its feedback in RL-based robotic control via a zero-shot sim-to-real approach with domain randomization. We demonstrate that learning and controlling with feedback from tactile sensor arrays at the gripper, both in simulation and reality, can enhance grasping stability, which leads to a significant improvement in robotic manipulation performance for a door opening task. In real-world experiments, the door open angle was increased by 45% on average for transferred policies with tactile sensing over those without it.

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ICRA Conference 2019 Conference Paper

Transfer Learning for Surgical Task Segmentation

Ya-Yen Tsai
Bidan Huang
Yao Guo 0002
Guang-Zhong Yang

In this paper, we present a novel approach for surgical task segmentation. A segmentation policy learns the correlations between features and segmentation points from manually labeled data. The most correlated features and rules for segmenting them are identified and learned. These form a complete set of segmentation policy. The proposed approach is developed to segment new but similar tasks through transfer learning. It is verified through applying the segmentation rule learned from the labeled data to segment other tasks. The performance of the proposed algorithm was evaluated by comparing the results against the ground truths. Experimental results demonstrate that our approach can achieve high segmentation rates with an accuracy of between 68. 8% - 81. 8%.

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IROS Conference 2017 Conference Paper

A vision-guided multi-robot cooperation framework for learning-by-demonstration and task reproduction

Bidan Huang
Menglong Ye
Su-Lin Lee
Guang-Zhong Yang

This paper presents a vision-based learning-by-demonstration approach for multi-robot manipulation. With this method, a vision system is involved in both the task demonstration and reproduction stages, and the speed and accuracy of the task reproduction are adapted according to the context of the demonstration. An expert first demonstrates how to use tools to perform a task, while the tool motion is observed using a vision system. The demonstrations are then encoded using a statistical model to generate a reference motion trajectory. Equipped with the same tools and the learned model, the robot is guided by vision to reproduce the task. The task performance was evaluated in terms of both accuracy and speed. However, simply increasing the robot's speed could decrease the reproduction accuracy. To this end, a dual-rate Kalman filter is employed to compensate for latency between the robot and vision system. More importantly, the robot speed is adapted according to the learned motion model. We demonstrate the effectiveness of our approach by performing two tasks: a trajectory reproduction task and a bimanual sewing task. We show that using our vision-based approach, the robots can conduct effective learning by demonstrations and perform accurate and fast task reproduction. The proposed approach is generalisable to other manipulation tasks, where bimanual or multi-robot cooperation is required.

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IROS Conference 2016 Conference Paper

A vision-guided dual arm sewing system for stent graft manufacturing

Bidan Huang
Alessandro Vandini
Yang Hu 0011
Su-Lin Lee
Guang-Zhong Yang

This paper presents an intelligent sewing system for personalized stent graft manufacturing, a challenging sewing task that is currently performed manually. Inspired by medical suturing robots, we have adopted a single-sided sewing technique using a curved needle to perform the task of sewing stents onto fabric. A motorized surgical needle driver was attached to a 7 d. o. f robot arm to manipulate the needle with a second robot controlling the position of the mandrel. A learning-from-demonstration approach was used to program the robot to sew stents onto fabric. The demonstrated sewing skill was segmented to several phases, each of which was encoded with a Gaussian Mixture Model. Generalized sewing movements were then generated from these models and were used for task execution. During execution, a stereo vision system was adopted to guide the robots and adjust the learnt movements according to the needle pose. Two experiments are presented here with this system and the results show that our system can robustly perform the sewing task as well as adapt to various needle poses. The accuracy of the sewing system was within 2mm.

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IROS Conference 2015 Conference Paper

Task-priority redundancy resolution for co-operative control under task conflicts and joint constraints

Yang Hu 0011
Bidan Huang
Guang-Zhong Yang

A fundamental problem with dual-arm robotic control is to find the coordinated motion resolution under high kinematic redundancy and intrinsic constraints of each robot. To solve this problem, this paper presents a multi-tasking, co-operative control framework, in which potential task conflicts and robot joint constraints are properly handled. Based on the relative Jacobian formulation, singularity-robust inverse kinematics and the scheme of null space distributing exceeded joint velocity, this work contributes by introducing a framework to handle multi-tasking conflicts both in task and joint space for dual-arm robots. Detailed validation of the proposed framework is first conducted by using a simulated dual-arm robot, followed by a demonstration on two 7-dof Kuka lightweight manipulators in a bimanual stent graft manufacturing task.

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ICRA Conference 2013 Conference Paper

Learning a real time grasping strategy

Bidan Huang
Sahar El-Khoury
Miao Li 0002
Joanna J. Bryson
Aude Billard

Real time planning strategy is crucial for robots working in dynamic environments. In particular, robot grasping tasks require quick reactions in many applications such as human-robot interaction. In this paper, we propose an approach for grasp learning that enables robots to plan new grasps rapidly according to the object's position and orientation. This is achieved by taking a three-step approach. In the first step, we compute a variety of stable grasps for a given object. In the second step, we propose a strategy that learns a probability distribution of grasps based on the computed grasps. In the third step, we use the model to quickly generate grasps. We have tested the statistical method on the 9 degrees of freedom hand of the iCub humanoid robot and the 4 degrees of freedom Barrett hand. The average computation time for generating one grasp is less than 10 milliseconds. The experiments were run in Matlab on a machine with 2. 8GHz processor.

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