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Diffusion for Multi-Embodiment Grasping

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2694-2701页

作者： Freiberg, Roman Qualmann, Alexander Vien, Ngo Anh Neumann, Gerhard BOSCH Corp Res D-71272 Renningen Germany Karlsruhe Inst Technol Autonomous Learning Robots Lab D-76137 Karlsruhe Germany

Grasping is a fundamental skill in robotics with diverse applications. However, current approaches for predicting successful grasps are often tailored to specific grippers, limiting their applicability when gripper designs change. To address this limitation, we explore the transfer of grasping strategies between various gripper designs, enabling the use of data from diverse sources. We present an approach based on equivariant diffusion that facilitates gripper-agnostic encoding of scenes containing graspable objects and gripper-aware decoding of grasp poses by integrating gripper geometry into the model. We also develop a dataset generation framework that produces cluttered scenes with variable-sized object heaps, improving the training of grasp synthesis methods. Experimental evaluation on diverse object datasets demonstrates the generalizability of our approach across gripper architectures, ranging from simple parallel-jaw grippers to humanoid hands, outperforming both single-gripper and multi-gripper State-of-the-Art methods.

关键词： Grippers Grasping robots Point cloud compression Manifolds Hands Training Robot vision systems Pipelines Internet deep learning in grasping and manipulation transfer learning

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A Convergence Guaranteed Multiple-Shooting DDP Method for Optimization-Based Robot Motion Planning

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第5期72卷 5001-5011页

作者： Wang, Yunlai Li, Hui Chen, Xuechao Huang, Xiao Jiang, Zhihong Beijing Inst Technol Sch Mechatron Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Key Lab Biomimet RobotsMinist Educ Beijing 100081 Peoples R China

Optimization-based motion planning plays a pivotal role in addressing high-dimensional robotic manipulation tasks. This article studies the multiple-shooting differential dynamic programming (MS-DDP) method to solve high-dimensional constrained problems with Markovian and non-Markovian processes. To tackle the non-Markovian shortest-path problem (SPP) in robot manipulation, we propose a fully multiple shooting strategy to handle the dependence between states. This strategy can solve the SPP efficiently by utilizing state augmentation at each time step to reformulate it into the Markovian process format. Moreover, we theoretically prove the quadratic convergence of the MS-DDP, providing a theoretical guarantee for the optimality of the planned trajectory. Experiments are conducted to demonstrate the optimality and efficiency of the MS-DDP method on the benchmarks of robot motion planning tasks. The real-world experimental results on a dual-arm robot validate its superiority in solving the high-dimensional shortest-path problem with complex constraints.

关键词： robots Planning Trajectory Costs Robot kinematics Jacobian matrices Convergence Cost function Vectors Robot motion Differential dynamic programming (DDP) dual-arm robot multiple-shooting (MS) strategy optimization-based motion planning shortest-path problem (SPP)

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A Compact and Low-Actuator Thrust System for Microgravity Flying Robot in Space Stations

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第1期72卷 629-638页

作者： Liu, Yunqi Li, Long Li, Hui Wang, Xiang Huang, Qiang Ceccarelli, Marco Beijing Inst Technol Sch Mechatron Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Key Lab Biomimet RobotsMinist Educ Beijing 100081 Peoples R China Beijing Inst Technol Sch Mechatron Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Key Lab Biomimet RobotsMinsit Educ Beijing 100081 Peoples R China China Acad Space Technol CAST Beijing 100094 Peoples R China Univ Roma Tor Vergata Dept Ind Engn Lab Robot Mechatron Via Politecn I-100133 Rome Italy

Compact and low-actuator microgravity flying robots represent a promising avenue for narrow-area inspection in space stations. Thus, this article presents an annular attitude nozzle-duct fan thrust system for a compact and low-actuator microgravity flying robotic platform. The proposed thrust system features a more compact structure that reduces the number of actuators, achieving six-degrees-of-freedom (DOF) motion by only six actuators. However, achieving propulsion and precise attitude adjustments depends on the coordinated operation of these duct fans and nozzles. Thus, a coordinate allocation controller has been meticulously devised in this article to address this challenge. This controller autonomously regulates the rotational speed and direction of the duct fans. Additionally, dynamic models of a robotic platform are derived for the control design. Subsequently, a four-DOF helium balloon platform was also designed to validate the efficacy of the coordinated allocation controller and cut costs. The proposed platform maximizes the robot's inherent symmetry along all three axes, thereby serving as an apt testbed for evaluating the feasibility of coupling attitude and propulsion control. The experiments verified the effectiveness of controllers for attitude control only, propulsion control only, and the coupling of attitude and propulsion control, thus demonstrating the robot's excellent controllability performance.

关键词： robots Fans Ducts Robot kinematics Torque Propulsion Space stations Annular attitude nozzle-duct fan (ANN-DF) system microgravity flying robot motion control nonthrough fluid channel (NTFC)

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Concept and Strategies: Equivalent Predictive Control and Handle Point Control for Bipedal-Vehicle Transformable robots Under Various Disturbances

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13470-13484页

作者： Dong, Chencheng Yu, Zhangguo Chen, Xuechao Lai, Junhang Liu, Jiayi Li, Chao Huang, Qiang Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Minist Educ Key Lab Biomimet Robots & Syst Beijing 100081 Peoples R China Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

Bipedal-vehicle transformable robots (BVTRs), equipped with driving wheels, combine the flexibility of bipedal locomotion with the speed of wheeled movement. However, maintaining balance across different formations under various external disturbances remains a significant challenge due to uncertain disturbance types and dynamic shifts between formations. To address these challenges, this paper introduces the concept of Equivalent Predictive Control (EPC), which models all disturbances as unified virtual wrenches and integrates them directly into the robot's predictive control model, treated as an inertia-varying single rigid body. By anticipating the future impact of disturbances, EPC enhances stability and enables simultaneous handling of various disturbances. To address the challenge of dynamic changes, contact variations, and shifting constraints during formation transitions, we propose Handle Point Control (HPC). HPC simplifies multi-task tracking by reducing joint space control to a set of virtual target points, called 'handle points', such as knees, hips, and shoulders. This method facilitates real-time formation switching by tracking different handle points. Experiments on the BVTR platform BHR8-2 validate the effectiveness of the proposed control strategies. Note to Practitioners-This paper addresses two critical challenges for applying BVTRs in real-world industrial scenarios: 1) managing various external disturbances, and 2) overcoming the complexities associated with changing dynamics and contact situations during formation transitions. The proposed EPC strategy unifies all disturbance types and integrates them into the robot's dynamic model, enhancing stability and adaptability across formations. The HPC method simplifies control by focusing on tracking key handle points, allowing smooth, real-time formation transitions without needing multiple optimization schemes. These methods can also be applied to other robotic systems that face similar control chal

关键词： robots Robot kinematics Adaptation models Predictive control Load modeling Vehicle dynamics Predictive models Automation Vectors Torque Transformable robots predictive control whole-body control optimizing

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Towards high mobility and adaptive mode transitions: Transformable wheel-biped humanoid locomotion strategy

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ISA TRANSACTIONS 2025年 158卷 184-196页

作者： Lai, Junhang Chen, Xuechao Yu, Zhangguo Chen, Zhuo Dong, Chencheng Liu, Xiaofeng Huang, Qiang Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Minist Educ Key Lab Biomimet Robots & Syst Beijing 100081 Peoples R China Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Beijing Inst Space Mech & Elect Beijing 100094 Peoples R China

Wheel-biped humanoid robots offer a promising solution that combines the bipedal locomotion and manipulation capabilities of humanoids with the mobility advantages of wheeled robots. However, achieving high mobility and adaptive wheel-foot transitions while maintaining essential bipedal functionality in a transformable wheel-biped configuration (TWBC) presents a significant challenge. To address this, this paper proposes a transformable wheel-humanoid framework (TWHF), which enhances traditional humanoid robots by incorporating a compact, decoupled wheeled subsystem. This design effectively balances high-speed wheeling, seamless mode transitions, and fundamental bipedal locomotion. A novel key phase decomposition (KPD) methodology is introduced to analyze and decouple transition motions, providing structured guidance for subsystem design, motion planning, and control. Transition reference motions are optimized using a particle swarm optimization-based motion optimization (PSOMO) approach, leveraging sagittal modeling to ensure dynamic stability and kinematic feasibility. Additionally, the proposed trunk-ankle collaborative control (TACC) strategy further enhances transition adaptability to terrain discrepancies. Extensive experiments conducted on the wheel-humanoid BHR8-2 validate the proposed TWHF, demonstrating stable hybrid locomotion across diverse terrains and achieving wheeling speeds exceeding 10 km/h.

关键词： Wheel-legged biped robot Wheel-humanoid Key phase decomposition Adaptive wheel-foot transition Transformable locomotion strategy

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Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-flight Attitude Control

arXiv

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arXiv 2025年

作者： Olsen, Jørgen Anker Malczyk, Grzegorz Alexis, Kostas Autonomous Robots Lab NTNU O.S. Bragstads Plass 2D Trondheim7034 Norway

Exploring planetary bodies with lower gravity, such as the moon and Mars, allows legged robots to utilize jumping as an efficient form of locomotion thus giving them a valuable advantage over traditional rovers for exploration. Motivated by this fact, this paper presents the design, simulation, and learning-based "in-flight" attitude control of Olympus, a jumping legged robot tailored to the gravity of Mars. First, the design requirements are outlined followed by detailing how simulation enabled optimizing the robot’s design - from its legs to the overall configuration - towards high vertical jumping, forward jumping distance, and in-flight attitude reorientation. Subsequently, the reinforcement learning policy used to track desired in-flight attitude maneuvers is presented. Successfully crossing the sim2real gap, extensive experimental studies of attitude reorientation tests are demonstrated. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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CERBERUS: autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge

Field Robotics

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Field Robotics 2025年 2卷 274-324页

作者： Marco Tranzatto Frank Mascarich Lukas Bernreiter Carolina Godinho Marco Camurri Shehryar Khattak Tung Dang Victor Reijgwart Johannes Löje David Wisth Samuel Zimmermann Huan Nguyen Marius Fehr Lukas Solanka Russell Buchanan Marko Bjelonic Nikhil Khedekar Mathieu Valceschini Fabian Jenelten Mihir Dharmadhikari Timon Hornberger Paolo De Petris Lorenz Wellhausen Mihir Kulkami Takahiro Miki Satchel Hirsch Markus Montenegro Christos Papachristos Fabian Tresoldi Jan Carius Giorgio Valsecchi Joonho Lee Konrad Meyer Xiangyu Wu Juan Nieto Andy Smith Marco Hutter Roland Siegwart Mark Mueller Maurice Fallon Kostas Alexis Robotic Systems Lab ETH Zurich Autonomous Robots Lab University of Nevada Reno | Norwegian University of Science and Technology Autonomous Systems Lab ETH Zurich Flyability Oxford Robotics Institute University of Oxford University of California Berkeley Sierra Nevada Corporation Direct correspondence to Marco Tranzatto

autonomous exploration of subterranean environments constitutes a major frontier for robotic systems, as underground settings present key challenges that can render robot autonomy hard to achieve. This problem has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, we present the CERBERUS system-of-systems, as a unified strategy for subterranean exploration using legged and flying robots. Our proposed approach relies on ANYmal quadraped as primary robots, exploiting their endurance and ability to traverse challenging terrain. For aerial robots, we use both conventional and collision-tolerant multirotors to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, we developed a complementary multimodal sensor-fusion approach, utilizing camera, LiDAR, and inertial data for resilient robot pose estimation. Individual robot pose estimates are refined by a centralized multi-robot map-optimization approach to improve the reported location accuracy of detected objects of interest in the DARPA-defined coordinate frame. Furthermore, a unified exploration path-planning policy is presented to facilitate the autonomous operation of both legged and aerial robots in complex underground networks. Finally, to enable communication among team agents and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station and wireless “breadcrumb” nodes deployed by the legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge's Tunnel and Urban Circuit events, along with the current limitations and the lessons learned for the benefit of the community.

关键词： robots Robot kinematics Legged locomotion Circuits Robot sensing systems Collision avoidance Base stations Location awareness autonomous aerial vehicles Pipelines

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A Novel Hand Teleoperation Method with Force and Vibrotactile Feedback Based on Dynamic Compliant Primitives Controller

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BIOMIMETICS 2025年第4期10卷 194-194页

作者： Hu, Peixuan Huang, Xiao Wang, Yunlai Li, Hui Jiang, Zhihong Beijing Inst Technol Sch Mechatron Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Key Lab Biomimet Robots & SystChinese Minist Educ Beijing 100081 Peoples R China

Teleoperation enables robots to perform tasks in dangerous or hard-to-reach environments on behalf of humans, but most methods lack operator immersion and compliance during grasping. To significantly enhance the operator's sense of immersion and achieve more compliant and adaptive grasping of objects, we introduce a novel teleoperation method for dexterous robotic hands. This method integrates finger-to-finger force and vibrotactile feedback based on the Fuzzy Logic-Dynamic Compliant Primitives (FL-DCP) controller. It employs fuzzy logic theory to identify the stiffness of the object being grasped, facilitating more effective manipulation during teleoperated tasks. Utilizing Dynamic Compliant Primitives, the robotic hand implements adaptive impedance control in torque mode based on stiffness identification. Then the immersive bilateral teleoperation system integrates finger-to-finger force and vibrotactile feedback, with real-time force information from the robotic hand continuously transmitted back to the operator to enhance situational awareness and operational judgment. This bidirectional feedback loop increases the success rate of teleoperation and reduces operator fatigue, improving overall performance. Experimental results show that this bio-inspired method outperforms existing approaches in compliance and adaptability during teleoperation grasping tasks. This method mirrors how human naturally modulate muscle stiffness when interacting with different objects, integrating human-like decision-making and precise robotic control to advance teleoperated systems and pave the way for broader applications in remote environments.

关键词： teleoperation dexterous robotic hand compliant manipulation adaptive impedance control fuzzy logic theory dynamic compliant primitives

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A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion

arXiv

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arXiv 2025年

作者： Qi, Haoxiang Yu, Zhangguo Chen, Xuechao Liu, Yaliang Yi, Chuanku Dong, Chencheng Meng, Fei Huang, Qiang The Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China The International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing100081 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization incorporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. 1) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework. Note to Practitioners-The motivation of this paper stems from the need to improve jumping performance of humanoid robots. By comprehensively considering factors such as robot posture, centroidal angular momentum, and landing foot placement, the algorithm enhances the robot's ability to navigate complex environments. This capability is crucial for applications that require overcoming obstacles, such as in search and rescue or inspection tasks. Improved jumping ability can significantly boost environmental adaptability, allowing robots to perform effectively in diverse conditions, and it also represents an exploration of the high-dynamic motion capabilities of humanoid robots. Future research will focus on integrating visual and perceptual information to enhance decision-making. Copyright © 2025, The Authors. All rights reserve

关键词： Anthropomorphic robots

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Forceful Aerial Manipulation Based on an Aerial Robotic Chain: Hybrid Modeling and Control

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 3711-3719页

作者： Huan Nguyen Alexis, Kostas Norwegian Univ Sci & Technol Dept Engn Cybernet Autonomous Robots Lab N-7491 Trondheim Norway Univ Nevada Autonomous Robots Lab Reno NV 89557 USA

This letter presents the system design, modeling, and control of the Aerial Robotic Chain Manipulator. This new robot design offers the potential to exert strong forces and moments on the environment, carry and lift significant payloads, and simultaneously navigate through narrow corridors. We contribute a hybrid modeling framework to model the system both in Free-flight mode, where the end-effector acts as a normal pendulum, and in Aerial Manipulation mode, where the system behaves as an inverted pendulum. Respective controllers are designed for both operating modes with stability guarantees provided by Lyapunov theory. The presented experimental studies include a task of valve rotation, a pick-and-release task, and the verification of load oscillation suppression to demonstrate the stability and performance of the system.

关键词： Aerial systems mechanics and control multirobot systems aerial robot manipulation

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