Robot Trajectron V3: A Probabilistic Shared Control Framework for SE(3) Manipulation

• URL: http://arxiv.org/abs/2607.09315v1
• Date: Fri, 10 Jul 2026 11:52:18 GMT
• Title: Robot Trajectron V3: A Probabilistic Shared Control Framework for SE(3) Manipulation
• Abstract summary: RT-V3 is a shared control framework designed for $SE(3)$ grasping tasks.<n>It learns a prior over user intent and combines it with real-time user commands to estimate the posterior intent distribution.<n>Experiments demonstrate that RT-V3 achieves high accuracy in trajectory prediction and competitive performance in reactive planning.
• Score: 5.090943130063491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We aim to address the challenge of teleoperating robotic arms for high-degree-of-freedom (high-DoF) manipulation tasks, which is cognitively demanding and error-prone, particularly when relying on low-bandwidth interfaces. We propose Robot Trajectron V3 (RT-V3), a probabilistic shared control framework designed for $SE(3)$ grasping tasks. RT-V3 formulates shared control as Bayesian inference by learning a prior over user intent and combining it with real-time user commands to estimate the posterior intent distribution. The prior models user intent as a distribution over future trajectories conditioned on past robot dynamics and visual scene context. The intent prior is parameterized by a transformer-based conditional generative model that reasons over point clouds and candidate grasp poses, together with a factorized translation-rotation representation that improves learning efficiency in high-dimensional action spaces. During execution, RT-V3 continuously estimates the posterior distribution over future trajectories by combining the learned intent prior with a user-command likelihood derived from the observed control input, enabling continuous intent refinement and shared assistance. Comprehensive experiments demonstrate that RT-V3 achieves high accuracy in trajectory prediction and competitive performance in reactive planning. Furthermore, real-world user studies indicate that RT-V3 significantly outperforms baseline methods in terms of success rate and efficiency, while substantially reducing the user's physical and mental workload.

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