发表机构
Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究让机器人通过投影视觉抽象通信，提出含21自由度灵巧手和影子自我模型的系统，能动态表达影子。通过自我探索学习映射，依目标优化配置，经模拟获可行运动，还引入多种优化，在多场景展示影子表达，建立相关框架。
AI 中文摘要
人类常通过身体的抽象形式进行交流，如影子、轮廓和反射。但机器人大多局限于通过物理形态表达。让机器人通过投影视觉抽象进行通信，不仅需考虑身体运动，还需考虑运动如何转化为观察者感知的外部表征。以影子为例，我们提出一个机器人系统，它使用有柔顺软皮肤的21自由度灵巧手和学习到的影子自我模型进行动态影子表达。软皮肤减少光泄漏以产生视觉上连续的轮廓，可微的自我模型通过任务无关的自我探索学习手部配置与投影影子外观之间的映射。给定目标影子图像或视频，机器人通过基于梯度的搜索优化手部配置，并通过碰撞感知模拟优化解决方案。为实现动态影子表现，还引入了表达区域目标、时间平滑正则化和基于关键帧的优化。我们在模拟和物理实验中展示了机器人在手语手势、手影戏和动物运动模仿中的影子表达。这些结果建立了一个框架，使机器人能够操纵自身的投影视觉抽象进行通信和视觉叙事。
英文摘要
Humans routinely communicate through abstractions of their bodies, including shadows, silhouettes, and reflections. Yet robots remain largely confined to expressing themselves through their physical morphology. Enabling robots to communicate through such projected visual abstractions requires reasoning not only about bodily motion but also about how that motion is transformed into an external representation perceived by an observer. Among these abstractions, shadows provide a particularly compelling example because they emerge directly from the robot's embodiment while remaining visually distinct from the body itself. Here, we present a robotic system capable of dynamic shadow expression using a 21-degree-of-freedom dexterous hand with compliant soft skin and a learned shadow self-model. The soft-skinned embodiment reduces light leakage to produce visually continuous silhouettes, while the differentiable self-model learns the mapping between hand configurations and projected shadow appearance through task-agnostic self-exploration. Given a target shadow image or video, the robot optimizes its hand configurations through gradient-based search over 1 the learned self-model and refines the solution through collision-aware simulation to obtain physically feasible motions. For dynamic shadow performance, we further introduce expressive-region objectives, temporal smoothness regularization, and keyframe-based optimization to preserve visually important motion cues while reducing optimization complexity. We demonstrate robotic shadow expression across sign-language gestures, hand-shadow puppetry, and animal motion imitation in both simulation and physical experiments. These results establish a framework for enabling robots to manipulate projected visual abstractions of themselves for communication and visual storytelling.
CommentsOur project website is at:https://generalroboticslab.com/shadow
