用少量尝试让大型软体机器人学会动态任务，不依赖复杂建模。
Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials
- 基于贝叶斯优化直接从压力指令优化任务目标，无需中间动力学模型。
- 仅用数次试验就实现抛掷、锤击等快速动态动作控制。
- 适合需要高适应性与快速响应的大型软体机器人应用场景。
软体机器人比传统刚性机器人更具灵活性、顺应性和适应性，且制造更轻便、成本更低。然而，其在实际应用中的推广受限于建模困难和本体感知传感器集成难题。大型软体机器人（约两米长）因惯性增大及重力影响，建模复杂度更高。现有方法常假设简化运动学或动力学模型，虽降低复杂性，却限制了软体机器人的通用能力，尤其难以支持抛掷、锤击等高速动态任务。为此，本文提出一种数据高效的贝叶斯优化控制策略，直接从指令压力优化任务目标函数，无需依赖近似动力学或运动学模型。通过仿真与真实实验验证，该方法仅需少量试错即可成功完成复杂动态任务，显著提升大型软体机器人的实用能力。
原文摘要 · Abstract (English)
Soft robots offer more flexibility, compliance, and adaptability than traditional rigid robots. They are also typically lighter and cheaper to manufacture. However, their use in real-world applications is limited due to modeling challenges and difficulties in integrating effective proprioceptive sensors. Large-scale soft robots ($\approx$ two meters in length) have greater modeling complexity due to increased inertia and related effects of gravity. Common efforts to ease these modeling difficulties such as assuming simple kinematic and dynamics models also limit the general capabilities of soft robots and are not applicable in tasks requiring fast, dynamic motion like throwing and hammering. To overcome these challenges, we propose a data-efficient Bayesian optimization-based approach for learning control policies for dynamic tasks on a large-scale soft robot. Our approach optimizes the task objective function directly from commanded pressures, without requiring approximate kinematics or dynamics as an intermediate step. We demonstrate the effectiveness of our approach through both simulated and real-world experiments.
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