让机器人自知能力边界，自动优化动作指令
Know your limits! Optimize the robot's behavior through self-awareness
- 用深度模型预测机器人模仿动作的成败
- 可预判99.29%的摔倒风险并选出最优动作
- 适合非专业用户快速控制人形机器人
随着人形机器人从实验室走向真实环境，需降低控制门槛以服务非专家用户。现有模仿学习算法虽追求高精度复现人类动作，但对参考动作质量敏感，且要求操作者预先简化动作以适配机器人能力。本文提出自感知模型SAW（Self-AWare），让机器人主动理解自身能力，动态调整参考动作。系统基于任务指令生成多个候选动作，通过跌倒概率、动作匹配度与平滑性等指标评分并优选最佳方案。SAW在预测跌倒方面准确率达99.29%，结合运动生成与控制实现端到端优化，显著提升机器人在任意任务下的行为表现。
原文摘要 · Abstract (English)
As humanoid robots transition from labs to real-world environments, it is essential to democratize robot control for non-expert users. Recent human-robot imitation algorithms focus on following a reference human motion with high precision, but they are susceptible to the quality of the reference motion and require the human operator to simplify its movements to match the robot's capabilities. Instead, we consider that the robot should understand and adapt the reference motion to its own abilities, facilitating the operator's task. For that, we introduce a deep-learning model that anticipates the robot's performance when imitating a given reference. Then, our system can generate multiple references given a high-level task command, assign a score to each of them, and select the best reference to achieve the desired robot behavior. Our Self-AWare model (SAW) ranks potential robot behaviors based on various criteria, such as fall likelihood, adherence to the reference motion, and smoothness. We integrate advanced motion generation, robot control, and SAW in one unique system, ensuring optimal robot behavior for any task command. For instance, SAW can anticipate falls with 99.29% accuracy. For more information check our project page: https://evm7.github.io/Self-AWare
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