DemoBridge：用于单视图人类演示重定向的循环内仿真工具包
DemoBridge: A Simulation-in-the-Loop Toolkit for Single-View Human Demonstration Retargeting
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中文总结 AI 辅助
DemoBridge工具包可将单视图人类手部演示转化为机器人手臂轨迹，核心是碰撞感知规划器，能综合考虑多种因素优化轨迹，经物理模拟器验证，还可用于策略学习，在实际任务和基准测试中评估了其性能。
中文摘要 AI 辅助
我们展示了DemoBridge，这是一个将人类手部演示的单视图RGB立体记录转化为可执行的、经过物理验证的机器人手臂轨迹的工具包。跨越实体差距进行重定向很困难。机器人手臂通过长的关节身体到达目标，其连杆的碰撞体积比手大得多。为映射的末端执行器姿态求解逆运动学通常无法得到无碰撞解决方案，并且轨迹在每个路径点都有此要求。单视图会增加噪声，使演示的参考不准确。DemoBridge的核心是一个单一的碰撞感知规划器。它一次优化整个关节轨迹，综合考虑替代抓取姿态、全臂和抓取物体的碰撞以及与演示路径的保真度。一个物理模拟器在循环中运行。它在每个阶段生成时进行验证，并在失败时回溯，因此无法按给定方式重现的演示会重新规划而不是丢弃。生成的动作序列动态稳定且忠实于演示操作。它还可兼作用于策略学习的现成模拟展开。抓取时间会自动推断，并且感知后端、机器人和管道阶段可从配置中互换。我们在三个实际演示任务上评估了全管道重定向，并在一个受控的合成基准上评估了规划器。我们的代码可在这个https网址获取。
英文摘要
We present DemoBridge, an toolkit that turns a single-view RGB stereo recording of a human hand demonstration into an executable, physics-validated robot-arm trajectory. Retargeting across the embodiment gap is hard. A robot arm reaches a target with a long, articulated body whose links carry far more collision volume than a hand. Solving inverse kinematics for the mapped end-effector pose often yields no collision-free solution, and a trajectory imposes this at every waypoint. A single view adds noise, leaving the demonstrated reference inaccurate. At the core of DemoBridge is a single collision-aware planner. It optimizes the whole joint trajectory at once, reasoning jointly over alternative grasp poses, whole-arm and grasped-object collision, and fidelity to the demonstrated path. A physics simulator runs in the loop. It validates each phase as it is produced and backtracks on failure, so a demonstration that cannot be reproduced as given is re-planned rather than discarded. The resulting action sequence is dynamically stable and faithful to the demonstrated manipulation. It also doubles as a ready-to-use simulation rollout for policy learning. Grasp timing is inferred automatically, and the perception backends, robot, and pipeline stages are swappable from configuration. We evaluate whole-pipeline retargeting on three real-demonstration tasks and the planner on a controlled synthetic benchmark. Our code is available at https://gitlab.kuleuven.be/u0123974/demo-bridge/ .
发表机构
- KU Leuven（库勒万大学）
- Toyota Motor Europe（丰田欧洲公司）
- Toyota Research Institute（丰田研究院）
机构由 AI 辅助整理，请以论文原文为准。
