用视觉学习的潜空间实现软体机器人的高精度开环控制
Accurate Open-Loop Control of a Soft Continuum Robot Through Visually Learned Latent Representations
- 通过视觉学习潜动力学，在潜空间中进行单步最优控制
- 结合注意力广播解码器，显著降低图像空间跟踪误差
- 首次实现可解释潜动态的长时程开环控制，适合机器人控制研究者
本工作针对软体连续体机器人（SCR）的开环控制问题，基于视频学习的潜动力学进行建模。采用先前工作的视觉振荡器网络（VONs），通过注意力广播解码器（ABCD）生成具有机制可解释性的二维振荡器潜变量。在无相机反馈条件下，于潜空间中执行开环单步最优控制，以追踪图像指定的路径点。一个交互式实时仿真器用于设计静态、动态及外推目标，并映射为模型特定的潜变量路径点。在双段气动软体机器人上，对带有和不带ABCD的Koopman、MLP及振荡器动力学模型在设定点与动态轨迹任务中进行了评估。基于ABCD的模型始终降低图像空间跟踪误差，其中基于VON和ABCD的Koopman模型达到最低均方误差。消融实验表明多个架构选择与训练设置影响控制性能。仿真压力测试进一步验证了静态保持、稳定外推平衡点以及自然恢复至静止状态的能力。据我们所知，这是首个展示可解释的视频学习潜动力学可实现可靠长时程开环控制软体机器人的研究。
原文摘要 · Abstract (English)
This work addresses open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics. Visual Oscillator Networks (VONs) from previous work are used, that provide mechanistically interpretable 2D oscillator latents through an attention broadcast decoder (ABCD). Open-loop, single-shooting optimal control is performed in latent space to track image-specified waypoints without camera feedback. An interactive SCR live simulator enables design of static, dynamic, and extrapolated targets and maps them to model-specific latent waypoints. On a two-segment pneumatic SCR, Koopman, MLP, and oscillator dynamics, each with and without ABCD, are evaluated on setpoint and dynamic trajectories. ABCD-based models consistently reduce image-space tracking error. The VON and ABCD-based Koopman models attains the lowest MSEs. Using an ablation study, we demonstrate that several architecture choices and training settings contribute to the open-loop control performance. Simulation stress tests further confirm static holding, stable extrapolated equilibria, and plausible relaxation to the rest state. To the best of our knowledge, this is the first demonstration that interpretable, video-learned latent dynamics enable reliable long-horizon open-loop control of an SCR.
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