Jul 2026· Clean Energy· Vol 10, pp. 169-181· 0 citations
Abstract
The transition towards sustainable energy systems demands accelerated materials discovery. This requirement drives the development of fully automated chemical laboratories. While multi-fingered dexterous hands offer the kinematic flexibility required to manipulate complex laboratory glassware, deploying them in safety-critical chemical environments remains a formidable challenge. Existing data-driven grasping models prioritize geometric stability but largely overlook strict functional constraints, often producing grasps that occlude vessel openings and cause sample contamination. To address this critical bottleneck, we present ChemGrasp, an affordance-aware dexterous grasping framework tailored for laboratory automation. Our approach introduces a task-specific affordance module during the inference phase of a generative model, employing an energy-based optimization function to strictly penalize semantic violations. Furthermore, to evaluate execution feasibility in constrained simulated workspaces, ChemGrasp integrates a system-level motion planning pipeline featuring a phased hand execution strategy, enabling collision-free and kinematically reachable trajectories in simulation. Extensive physics-based simulations demonstrate that ChemGrasp significantly elevates the Safe Success Rate (SSR) by eliminating functional violations, reliably executing dynamic grasp sequences in both floating and full-pipeline tabletop settings. Ultimately, this framework demonstrates a simulation-validated step toward adapting robotic dexterity to laboratory safety protocols for autonomous clean energy research.
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
CoToGrasp is a novel generative framework that synthesizes diverse, stable grasps strictly conditioned on specific contact topologies, and introduces a feature-based canonical workspace that projects local object features into a unified gripper-centric domain, effectively decoupling the semantic functional intent from the arbitrary object geometry.
Julien Mérand, Boris Meden, Liming Chen et al.· 1 citation
The transition toward high-mix low-volume manufacturing demands flexibility in robotic manipulation. However, conventional vision systems remain a bottleneck, requiring extensive data collection and model retraining whenever a new object is introduced to the production line. To overcome this rigidity, we present xperception, a zero-shot 6D pose estimation technology that eliminates the need for object-specific fine-tuning and laborious data annotation. By directly utilizing typical CAD models and integrating the rich semantic features of foundation models (e.g. DINOv2, GeDi), xperception achieves millimeter-accurate 6D pose estimation. xperception showed robustness against severe occlusions in industrial tasks like bin picking and is engineered for deployment on industrial edge hardware, such as NVIDIA Jetson Thor. Validated at a TRL of 6, the core methodology behind xperception is based on the FreeZe algorithm, which won the international BOP Challenge 2024, paving the way for scalable, plug-and-play robotic automation in unstructured high-mix low-volume manufacturing industries.
M. Bortolon, Andrea Caraffa, Alice Fasoli et al.· arXiv.org· 0 citations
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
Traditional task-and-motion planning (TAMP) approaches primarily focus on defining sequences of actions along with the necessary geometric and kinematic constraints to execute long-horizon tasks. However, their applicability in real-world settings is limited, as they typically assume simplified object models that overlook key physical properties critical for the successful execution of contact-rich tasks. Moreover, they often use sub-symbolic reasoning during motion planning, which drastically increases planning time and decreases overall success rates. We propose a method that leverages a TAMP approach, defining object-centric abstractions of execution constraints, called Unified TAMP (U-TAMP), to execute robotic tasks involving interactions among objects with heterogeneous shapes, sizes, and materials. Using a Vision-Language Model (VLM), we generate abstractions of inter-object affordances for characterizing physical interaction constraints between objects in contact-rich tasks, such as grasp and support constraints. These constraints are used to enrich the U-TAMP planning domain to deal with objects with variable physical properties. We perform experiments in simulated kitchen table organization scenarios and compare our results with those of the original U-TAMP, as well as a state-of-the-art VLM-based planner that leverages common sense knowledge of objects'affordances for plan generation. Our approach achieves significantly higher planning success rates and improves planning times by one to two orders of magnitude compared to other methods.
Pouya P. Niaz, J. Piater, Alejandro Agostini· 0 citations
Language-guided robotic grasping has made significant progress in semantic understanding, but existing methods often rely on open-loop execution strategies and struggle to handle physical disturbances such as object collisions, target displacement, and transportation slippage. To address this problem, this paper proposes a stability-aware dynamic recovery mechanism, named SADR. Based on a multi-threaded decoupled architecture, SADR decouples semantic planning, target tracking, and execution control, and constructs a two-stage stability criterion through pre-closure displacement checking and post-closure force/current feedback verification. When target instability, missed grasping, or slippage is detected, the system performs local trajectory correction and re-grasping based on real-time tracking results, without restarting global semantic planning. Experiments in PyBullet show that SADR significantly improves the grasping success rate under high-density disturbance scenarios while reducing the average task completion time. This study provides an effective closed-loop recovery solution for improving the reliability of robotic grasping tasks in complex simulation environments.
Chun-Cheng Zhang, Lei Sun· 2026 IEEE 27th China Confere...· 0 citations
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