发表机构
KU Leuven; Flanders Make（鲁汶大学; 法兰德斯制造）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对远程操作机器人手臂执行高自由度操作任务的难题（认知要求高、易出错、依赖低带宽接口），提出概率共享控制框架RT-V3，通过贝叶斯推理结合用户意图先验与实时命令估计后验分布，实验证明其在多方面表现出色，优于基线方法。
AI 中文摘要
我们旨在应对远程操作机器人手臂执行高自由度操作任务的挑战，此类任务认知要求高且易出错，尤其是依赖低带宽接口时。我们提出了机器人轨迹生成器V3（RT-V3），这是一个为SE(3)抓取任务设计的概率共享控制框架。RT-V3通过学习用户意图先验并将其与实时用户命令相结合来估计后验意图分布，从而将共享控制表述为贝叶斯推理。先验将用户意图建模为基于过去机器人动力学和视觉场景上下文的未来轨迹上的分布。意图先验由基于点云和候选抓取姿态进行推理的基于Transformer的条件生成模型以及在高维动作空间中提高学习效率的分解平移-旋转表示进行参数化。在执行过程中，RT-V3通过将学习到的意图先验与从观察到的控制输入中导出的用户命令似然性相结合，不断估计未来轨迹的后验分布，实现持续的意图细化和共享辅助。综合实验表明，RT-V3在轨迹预测方面具有高精度，在反应式规划方面具有有竞争力的性能。此外，实际用户研究表明，RT-V3在成功率和效率方面显著优于基线方法，同时大幅减轻了用户的身心工作量。
英文摘要
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.
