让残障用户用简接口操控机械臂时，能自动还原出更优的完整运动轨迹。
Interface-Aware Trajectory Reconstruction of Limited Demonstrations for Robot Learning
- 基于任务、环境与接口约束，将受限操作数据还原到全自由度空间。
- 实测显示重构轨迹速度更快、效率更高，且符合用户真实意图。
- 适合残障辅助机器人、低维控制接口优化等场景研究者参考。
辅助机器人可为严重运动障碍者提供行动能力。通常，这些用户通过低维接口（如1维吸吹接口）控制高自由度机器人（如6自由度机械臂），导致同一时间只能操作部分控制维度，造成人为限制，使示范动作偏离用户真实意图。为此，我们提出一种轨迹重构算法，综合考虑任务、环境与接口约束，将受限示范提升至机器人完整控制空间。在真实世界中，使用2维操纵杆和1维吸吹接口完成以日常生活活动（ADL）为灵感的任务，操控两台不同的7自由度机械臂进行验证。分析重构轨迹及生成的控制策略表明，重构后的轨迹比原始受限轨迹更快、更高效，同时保留用户偏好。
原文摘要 · Abstract (English)
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.
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