研究人员提出了Transformer Transformer模型，这是一个统一的AI系统，能够根据目标运动演示自动生成优化的机器人设计，包括每个连接、关节、马达和惯性属性1。该模型在cloth flinging任务中实现了显著性能提升，相比ALOHA2原始设计，追踪误差降低73%，最大关节速度降低30%1。
该模型基于RoboTokens统一标记化框架，采用扩散变换器架构，并通过Dynamics Self-Guidance机制实现零样本奖励优化1。RoboTokens框架涵盖11个MuJoCo机器人，质量范围从0.65 kg至67.5 kg，拥有6到35个活跃关节1。每个机器人被转换为28-101个RoboTokens序列1。同一网络通过改变掩码方式可扮演三个角色：生成器、评论器和跨实体控制器1。
研究表明，性能平台期约在一分钟推理时间后出现1。在强化学习专家数据生成方面，每个离散设计选择需要16小时的A100计算成本1。
Researchers have introduced Transformer Transformer, a unified artificial intelligence system capable of automatically generating optimized robot designs based on target motion demonstrations 1. The model specifies every connection, joint, motor, and inertial property to match the demonstrated motion, representing a significant advance in robot co-design automation 1.
Testing on a cloth flinging task demonstrated substantial performance improvements compared to the original ALOHA2 design, with tracking error reduced by 73% and maximum joint velocity decreased by 30% 1. The system operates on the RoboTokens unified tokenization framework, which encompasses 11 MuJoCo robots with masses ranging from 0.65 kilograms to 67.5 kilograms and between 6 and 35 active joints 1. Each robot is converted into a sequence of 28 to 101 RoboTokens 1.
The architecture employs a diffusion transformer design enhanced by a Dynamics Self-Guidance mechanism that enables zero-shot reward optimization 1. A single network performs three distinct roles by varying masking strategies: generator, critic, and cross-entity controller 1. Performance plateaus approximately after one minute of inference time 1. Creating reinforcement learning expert data for the system carries significant computational costs, requiring 16 hours of A100 GPU computing per discrete design choice 1.
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