Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-8· 0 citations· 25 references
Abstract
The problem of shaping soft objects is widespread in industrial, medical, and household settings. Hence, robotic Deformable Object Manipulation (DOM) is a field of research that has recently emerged to improve robotic systems’ ability to handle such objects. Indeed, human-robot collaboration is also relevant to applications featuring soft objects, since the decision-making and dexterity of a human operator are currently beyond reach.Our aim is to assess the feasibility of using fast finite element inverse simulation in collaborative shaping tasks. To this end, we propose a computationally efficient method for controlling the shape of an object grasped at both ends. In our experimental setup, a leader robot moves freely along unplanned trajectories, while a controlled robot maintains the desired shape despite these perturbations. We reach an update frequency of 20 Hz for the inverse simulation, with average steady-state shape errors below 10 mm in most cases. Those satisfying results allow us to envision our next milestone: the deployment of the inverse simulation in real human-robot shaping tasks.
This hands-on course presents a workflow for configuring robot simulations using OpenUSD and PhysX in NVIDIA Isaac Sim, with an emphasis on physics tuning, stability, and asset authoring best practices, and situates OpenUSD’s composability within the broader physical AI ecosystem.
Ji Yuan Feng, Alexandra Kissel· Proceedings of the Special I...· 0 citations
This work added YOLO-based object and hand detection, stereo vision-based localization using the robot's built-in low-resolution fisheye cameras, and task-specific corrections for grasp execution to form a novel calibration-based grasping pipeline that does not require RGB-D cameras, motion capture, or external tracking systems.
Dual-arm robots often encounter difficulties when handling easily deformable or structurally complex objects using traditional grasping-based manipulation. In addition, grasping and releasing operations introduce significant time overhead. To address these limitations, this paper proposes a vision-based predictive control framework for dual-arm nonprehensile transportation. The proposed method employs a hybrid end effector design that integrates an elastic tether with a tray, enabling flexible and stable transportation without direct grasping. A predictive control strategy is adopted to optimize dual-arm motion trajectories on the move under kinematic and safety constraints. To further enhance coordination accuracy, a direct visual servoing scheme is incorporated to dynamically regulate the arm velocities, minimizing relative motion between the end effectors and the object. This effectively suppresses oscillations induced by the elastic tether. Both simulation and experimental results demonstrate that the proposed approach ensures convergence to desired states and achieves continuous, stable, and safe object transportation, even in the presence of disturbances.
Chang Liu, Yuan Yang, Panfeng Huang et al.· 2026 IEEE International Conf...· 0 citations
Dexterous manipulation with multi-fingered robot hands promises human-level dexterity, but collecting large-scale dexterous robot hand data remains difficult. Learning from human demonstrations has emerged as a scalable alternative to robot teleoperation, providing strong priors on object interaction and contact strategies. Recent sim-to-real RL methods incorporate such priors, but often (i) omit rewards that explicitly incentivize precise contact, yielding weak real-world performance, and/or (ii) generalize poorly to unseen object instances. We propose DemoMimic (Dexterous Motion Mimic), a policy that manipulates objects by focusing on their geometry local to the contact points. Its contact-centric rewards encourage precise contact and improve sim-to-real consistency, yielding a single real-world policy that transfers across objects of varying shape, scale, mass, and friction wherever local contact structure is preserved. Real-world ablations show that DemoMimic achieves 71% success across 16 objects, four tasks, and two robot-hand embodiments, with the smallest sim-to-real drop compared to baselines.
Satvik Sharma, Samrat Sahoo, Huang Huang et al.· 0 citations
An adaptive scheme that estimates the kinematic relationship between a robot's joints and the task features it senses online, using only joint-angle sensing and a wrist-mounted force/torque sensor, with no exteroceptive measurement of the tool tip is developed.
This work proposes a real-world bimanual grasping framework that includes a multimodal dataset capturing joint angles, visual observations and force signals; a Denoising Diffusion Probabilistic Model (DDPM)-based module that generates joint-level grasp configurations from segmented point clouds; and an execution strategy that integrates motion planning with online grasp refinement to ensure physical stability and feasibility.
Ziming Li, Mingxuan Wu, Jiaqi Zhang et al.· 0 citations
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