Assistive robots offer agency to humans with severe motor impairments. Often, these users control high-DoF robots through low-dimensional interfaces, such as using a 1-D sip-and-puff interface to operate a 6-DoF robotic arm. This mismatch results in having access to only a subset of control dimensions at a given time, imposing unintended and artificial constraints on robot motion. As a result, interface-limited demonstrations embed suboptimal motions that reflect interface restrictions rather than user intent. To address this, we present a trajectory reconstruction algorithm that reasons about task, environment, and interface constraints to lift demonstrations into the robot's full control space. We evaluate our approach using real-world demonstrations of ADL-inspired tasks performed via a 2-D joystick and 1-D sip-and-puff control interface, teleoperating two distinct 7-DoF robotic arms. Analyses of the reconstructed demonstrations and derived control policies show that lifted trajectories are faster and more efficient than their interface-constrained counterparts while respecting user preferences.

翻译：辅助机器人为重度运动障碍患者提供了自主行动能力。这类用户通常通过低维控制界面（如使用一维吸吹接口操作六自由度机械臂）来操控高自由度机器人。这种维度不匹配导致用户在给定时刻只能访问控制维度的一个子集，从而对机器人运动施加了非预期的人为约束。因此，受界面限制的演示数据会嵌入次优运动轨迹，这些轨迹反映的是界面限制而非用户真实意图。为解决该问题，我们提出一种轨迹重建算法，该算法通过推理任务约束、环境约束与界面约束，将演示数据提升至机器人的完整控制空间。我们使用真实世界演示数据评估该方法，演示任务受日常生活活动启发，通过二维摇杆和一维吸吹控制接口远程操作两种不同的七自由度机械臂。对重建演示数据及衍生控制策略的分析表明：提升后的轨迹在尊重用户偏好的同时，相较于界面受限的原始轨迹具有更快的执行速度和更高的运动效率。