TR2026-069
Robotic manipulators excel in structured envi- ronments but face substantial challenges in unstructured and dynamic settings. This paper presents SplatCtrl, a unified framework for real-time scene reconstruction and reactive robot motion generation to enable collision-free robotic arm control in previously unseen and continuously changing environments. Building on 3D Gaussian Splatting (3D-GS), we introduce a hybrid voxel-based filtering and dynamic Gaussian relocation strategy that supports efficient scene reconstruction from RGB- D streams while accommodating environmental changes. For safe and reactive control, we further propose a method for deriving continuous signed distance functions from isotropic Gaussians, providing stable and differentiable collision prob- ability estimates that bridge classical distance fields with the modern implicit representation. These continuous distance met- rics are incorporated into control barrier functions, resulting in a unified perception–action coupling framework that supports smooth and reliable real-time motion generation in response to scene changes. Experimental validation in simulation, on physical robot, and within shared human–robot workspace demonstrates the framework’s effectiveness, achieving inte- grated scene reconstruction and reactive control in uncertain, and dynamic environments.
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- , "Robotic manipulators excel in structured envi- ronments but face substantial challenges in unstructured and dynamic settings. This paper presents SplatCtrl, a unified framework for real-time scene reconstruction and reactive robot motion generation to enable collision-free robotic arm control in previously unseen and continuously changing environments. Building on 3D Gaussian Splatting (3D-GS), we introduce a hybrid voxel-based filtering and dynamic Gaussian relocation strategy that supports efficient scene reconstruction from RGB- D streams while accommodating environmental changes. For safe and reactive control, we further propose a method for deriving continuous signed distance functions from isotropic Gaussians, providing stable and differentiable collision prob- ability estimates that bridge classical distance fields with the modern implicit representation. These continuous distance met- rics are incorporated into control barrier functions, resulting in a unified perception–action coupling framework that supports smooth and reliable real-time motion generation in response to scene changes. Experimental validation in simulation, on physical robot, and within shared human–robot workspace demonstrates the framework’s effectiveness, achieving inte- grated scene reconstruction and reactive control in uncertain, and dynamic environments.", 2026 IEEE International Conference on Robotics & Automation (ICRA), June 2026.BibTeX TR2026-069 PDF
- @inproceedings{Jain2026jun,
- author = {Jain, Siddarth and Choi, Ho Jin},
- title = {{Robotic manipulators excel in structured envi- ronments but face substantial challenges in unstructured and dynamic settings. This paper presents SplatCtrl, a unified framework for real-time scene reconstruction and reactive robot motion generation to enable collision-free robotic arm control in previously unseen and continuously changing environments. Building on 3D Gaussian Splatting (3D-GS), we introduce a hybrid voxel-based filtering and dynamic Gaussian relocation strategy that supports efficient scene reconstruction from RGB- D streams while accommodating environmental changes. For safe and reactive control, we further propose a method for deriving continuous signed distance functions from isotropic Gaussians, providing stable and differentiable collision prob- ability estimates that bridge classical distance fields with the modern implicit representation. These continuous distance met- rics are incorporated into control barrier functions, resulting in a unified perception–action coupling framework that supports smooth and reliable real-time motion generation in response to scene changes. Experimental validation in simulation, on physical robot, and within shared human–robot workspace demonstrates the framework’s effectiveness, achieving inte- grated scene reconstruction and reactive control in uncertain, and dynamic environments.}},
- booktitle = {2026 IEEE International Conference on Robotics \& Automation (ICRA)},
- year = 2026,
- month = jun,
- url = {https://www.merl.com/publications/TR2026-069}
- }
- , "Robotic manipulators excel in structured envi- ronments but face substantial challenges in unstructured and dynamic settings. This paper presents SplatCtrl, a unified framework for real-time scene reconstruction and reactive robot motion generation to enable collision-free robotic arm control in previously unseen and continuously changing environments. Building on 3D Gaussian Splatting (3D-GS), we introduce a hybrid voxel-based filtering and dynamic Gaussian relocation strategy that supports efficient scene reconstruction from RGB- D streams while accommodating environmental changes. For safe and reactive control, we further propose a method for deriving continuous signed distance functions from isotropic Gaussians, providing stable and differentiable collision prob- ability estimates that bridge classical distance fields with the modern implicit representation. These continuous distance met- rics are incorporated into control barrier functions, resulting in a unified perception–action coupling framework that supports smooth and reliable real-time motion generation in response to scene changes. Experimental validation in simulation, on physical robot, and within shared human–robot workspace demonstrates the framework’s effectiveness, achieving inte- grated scene reconstruction and reactive control in uncertain, and dynamic environments.", 2026 IEEE International Conference on Robotics & Automation (ICRA), June 2026.
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Abstract:
Robotic manipulators excel in structured environments but face substantial challenges in unstructured and dynamic settings. This paper presents SplatCtrl, a unified framework for real-time scene reconstruction and reactive robot motion generation to enable collision-free robotic arm control in previously unseen and continuously changing environments. Building on 3D Gaussian Splatting (3D-GS), we introduce a hybrid voxel-based filtering and dynamic Gaussian relocation strategy that supports efficient scene reconstruction from RGB- D streams while accommodating environmental changes. For safe and reactive control, we further propose a method for deriving continuous signed distance functions from isotropic Gaussians, providing stable and differentiable collision prob- ability estimates that bridge classical distance fields with the modern implicit representation. These continuous distance metrics are incorporated into control barrier functions, resulting in a unified perception–action coupling framework that supports smooth and reliable real-time motion generation in response to scene changes. Experimental validation in simulation, on physical robot, and within shared human–robot workspace demonstrates the framework’s effectiveness, achieving integrated scene reconstruction and reactive control in uncertain, and dynamic environments.