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Zida Zhou

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7 papers
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7

IROS Conference 2025 Conference Paper

Biomechanically-Inspired Bipedal Robot Locomotion via Hybrid Gait Representation and Model-Guided Reinforcement Learning

  • Lijie Xie
  • Haomin Rong
  • Zujian Chen
  • Zida Zhou
  • Shaolin Mo
  • Hui Cheng

Achieving stable and natural locomotion in bipedal robots, comparable to that of humans and animals, remains a long-standing challenge in robotics. In this work, we propose a bio-inspired low-level control framework that streamlines the generation of naturalistic gait patterns while ensuring adaptability. Our approach begins with the design of a low-dimensional gait representation that captures key characteristics of human and animal locomotion. This representation is then integrated with the Linear Inverted Pendulum Model (LIPM) to form an abstract yet effective motion descriptor. Serving as a kinematic reference within a reinforcement learning (RL) framework, this descriptor enables the training of control policies that strike a balance between biomechanical realism and adaptability. Rather than strictly adhering to predefined gait trajectories, the learned policies dynamically adjust to optimize both stability and velocity tracking. As a result, our method enables bipedal robots to exhibit smooth, biomechanically realistic locomotion while enhancing stability and adaptability. We validate the proposed framework through real-world experiments on our bipedal robot, demonstrating its ability to achieve stable and efficient locomotion.

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IROS Conference 2025 Conference Paper

Design of an Affordable, Fully-Actuated Biomimetic Hand for Dexterous Teleoperation Systems

  • Zhaoliang Wan
  • Zida Zhou
  • Zetong Bi
  • Zehui Yang
  • Hao Ding
  • Hui Cheng

This paper addresses the scarcity of affordable, fully-actuated five-fingered hands for dexterous teleoperation, which is crucial for collecting large-scale real-robot data within the "Learning from Demonstrations" paradigm. We introduce the prototype version of the RAPID Hand, the first low-cost, 20-degree-of-actuation (DoA) dexterous hand that integrates a novel anthropomorphic actuation and transmission scheme with an optimized motor layout and structural design to enhance dexterity. Specifically, the RAPID Hand features a universal phalangeal transmission scheme for the non-thumb fingers and an omnidirectional thumb actuation mechanism. Prioritizing affordability, the hand employs 3D-printed parts combined with custom gears for easier replacement and repair. We assess the RAPID Hand’s performance through quantitative metrics and qualitative testing in a dexterous teleoperation system, which is evaluated on three challenging tasks: multi-finger retrieval, ladle handling, and human-like piano playing. The results indicate that the RAPID Hand’s fully actuated 20-DoF design holds significant promise for dexterous teleoperation.

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NeurIPS Conference 2025 Conference Paper

RAPID Hand: Robust, Affordable, Perception-Integrated, Dexterous Manipulation Platform for Embodied Intelligence

  • Zhaoliang Wan
  • Zetong Bi
  • Zida Zhou
  • Hao Ren
  • Yiming Zeng
  • Yihan Li
  • Lu Qi
  • Xu Yang

This paper addresses the scarcity of low-cost but high-dexterity platforms for collecting real-world multi-fingered robot manipulation data towards generalist robot autonomy. To achieve it, we propose the RAPID Hand, a co-optimized hardware and software platform where the compact 20-DoF hand, robust whole-hand perception, and high-DoF teleoperation interface are jointly designed. Specifically, RAPID Hand adopts a compact and practical hand ontology and a hardware-level perception framework that stably integrates wrist-mounted vision, fingertip tactile sensing, and proprioception with sub-7 ms latency and spatial alignment. Collecting high-quality demonstrations on high-DoF hands is challenging, as existing teleoperation methods struggle with precision and stability on complex multi-fingered systems. We address this by co-optimizing hand design, perception integration, and teleoperation interface through a universal actuation scheme, custom perception electronics, and two retargeting constraints. We evaluate the platform’s hardware, perception, and teleoperation interface. Training a diffusion policy on collected data shows superior performance over prior works, validating the system’s capability for reliable, high-quality data collection. The platform is constructed from low-cost and off-the-shelf components and will be made public to ensure reproducibility and ease of adoption.

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IROS Conference 2025 Conference Paper

RMCC: Rigid Multi-joint Coupled Continuum Structure for Bionic Robots

  • Zida Zhou
  • Ying Wu
  • Zujian Chen
  • Zetong Bi
  • Hui Cheng

Continuum robots, inspired by biological structures such as spines and tails, have attracted significant attention due to their flexibility and ability to perform complex tasks in confined and dynamic environments. However, traditional flexible continuum robots often encounter challenges such as non-linearity, hysteresis, and limited load-bearing capacity, which can compromise their precision and effectiveness in practical applications. To address these limitations, this paper presents a novel bionic continuum mechanism: Rigid Multi-joint Coupled Continuum Structure(RMCC), which employs a rigid mechanical transmission mode to couple all joints, achieving coordinated movement of multiple joints. Its rigid structural composition and transmission method provide it with high precision and load capacity. The coordinated motion of the joints endows it with the dexterity of a continuum mechanism, while also enabling efficient and precise control with a minimal number of motors. The modular joint design improves the system’s scalability and adaptability, enabling a wide range of configurations to suit diverse robotic applications. The feasibility and effectiveness of the proposed system are validated through a series of bio-inspired experiments, including lizardlike crawling, falling-cat movement, and adaptive grasping like birds. The experimental results confirm that the RMCC exhibits the flexibility and adaptability of animals, demonstrating its potential for diverse bionic robotics applications.

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ICRA Conference 2024 Conference Paper

Robust and Energy-Efficient Control for Multi-task Aerial Manipulation with Automatic Arm-switching

  • Ying Wu
  • Zida Zhou
  • Mingxin Wei
  • Hui Cheng

Aerial manipulation has received increasing research interest with wide applications of drones. To perform specific tasks, robotic arms with various mechanical structures will be mounted on the drone. It results in sudden disturbances to the aerial manipulator when switching the robotic arm or interacting with the environment. Hence, it is challenging to design a generic and robust control strategy adapted to various robotic arms when achieving multi-task aerial manipulation. In this paper, we present a learning-based control algorithm that allows online trajectory optimization and tracking to accomplish various aerial interaction tasks without manual adjustment. The proposed energy-saved trajectory planning approach integrates coupled dynamics model with a single rigid body to generate the energy-efficient trajectory for the aerial manipulator. Addressing the challenges of precise control when performing aerial manipulation tasks, this paper presents a controller based on deep neural networks that classifies and learns accurate forces and moments caused by different robotic arms and interactions. Moreover, the forces arising from robotic arm motions are delicately used as part of the drone’s power to save energy. Extensive real-world experiments demonstrate that the proposed method can adapt to various robotic arms and interactions when performing multi-task aerial manipulation.

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ICRA Conference 2023 Conference Paper

Dynamic Locomotion of a Quadruped Robot with Active Spine via Model Predictive Control

  • Wanyue Li
  • Zida Zhou
  • Hui Cheng

As an active spine introduces more degree of freedoms (DOFs) as well as time-varying inertia, locomotion control of spined quadruped robots is challenging. Direct optimization on the full dynamics model causes prohibitive calculation time and is difficult to apply to embedded platforms. Model predictive control (MPC)-based on SRB dynamics is a prevalent approach for ordinary quadruped robots, regarding the whole robot as a single rigid body (SRB). However, the approach ignores the changes of the center of mass (CoM) and inertia, which seriously affects the robot's stability and could not be used in spined quadruped robots directly. To resolve the above issue, this paper presents an MPC approach that considers the movements of the spine in the SRB model. Since the mass of the robot is concentrated on its body, the whole robot is modelled as an unactuated SRB with fully-actuated internal spine joints. MPC finds the optimal ground reaction forces (GRFs) based on the SRB dynamics, in which the missing spine part is complemented by the pre-defined spine joints' states and corresponding inertia sequence. According to the GRFs, the full dynamic model calculates the precise joint torques. In addition, a quadruped robot with a 3-DOF active spine, Yat-sen Lion, is developed. With the presented approach, experimental results illustrate that Yat-sen Lion freely achieves bending, arching, and turning behaviors while trotting at speeds of 3. 8 m/s in simulations and 0. 5 m/s in real-world experiments.

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ICRA Conference 2021 Conference Paper

Control of an Aerial Manipulator Using a Quadrotor with a Replaceable Robotic Arm

  • Zizhen Ouyang
  • Ruidong Mei
  • Zisen Liu
  • Mingxin Wei
  • Zida Zhou
  • Hui Cheng

Control of an aerial manipulator is challenging due to the decentralized dynamics of the aerial vehicle and the robotic arm. It is generally complex to adjust the controller of the aerial manipulator when replacing a different robotic arm. This paper presents a flexible control scheme for a quadrotor-based aerial manipulator equipped with a replaceable robotic arm. To analyze the dynamic characteristics during grasping, the model of the aerial manipulator is decentralized including the models of a quadrotor and the centroid of an n-DOF robotic arm. The interaction effect of a moving robotic arm on the quadrotor is considered by analyzing the varying centroid of the robotic arm. Based on the modeling of the aerial manipulator, a control scheme integrating a linear model predictive control (LMPC) and a feedforward controller is presented to accurately control the motion of the aerial platform. The LMPC controls the aerial vehicle to follow the desired trajectory, and a feedforward controller keeps the aerial platform hovering stably during grasping. Practical experiments with two different robotic arms are performed. Experimental results show that the proposed modeling and control scheme provides a flexible and effective approach for an aerial manipulator with a replaceable robotic arm.

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