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arXiv 2007.01520cs.ROcs.LG
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第一步:四足机器人运动的语义约束潜空间控制

First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion

Applied AI Lab (A2I)(应用人工智能实验室(A2I));Dynamic Robot Systems (DRS)(动态机器人系统(DRS));Oxford Robotics Institute (ORI), University of Oxford(牛津大学牛津机器人研究所(ORI))

Alexander L. Mitchell, Martin Engelcke, Oiwi Parker Jones, David Surovik, Siddhant Gangapurwala, Oliwier Melon, Ioannis Havoutis, Ingmar Posner

摘要:

传统的四足动物控制方法通常采用简化的手衍生模型。这显著降低了机器人的能力,因为其有效的运动范围被缩减。此外,Kinodynamic约束通常是不可微的,并且难以在优化方法中实现。在这项工作中,这些挑战是解决框架四足动物控制优化结构化的潜在空间。深度生成模型捕获可行的关节配置的统计表示,而复杂的动态和终端约束通过高级语义指标表示,并由在潜在空间上操作的学习分类器表示。因此,复杂的约束呈现微分和评估的数量级快于分析方法。我们验证的可行性运动轨迹优化使用我们的方法在模拟和现实世界中的ANYmal四足动物。我们的研究结果表明,这种方法是能够产生平滑和可实现的轨迹。据我们所知,这是第一次潜在的空间控制已成功地应用于一个复杂的,真正的机器人平台。

英文摘要:

Traditional approaches to quadruped control frequently employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators and represented by learned classifiers operating upon the latent space. As a consequence, complex constraints are rendered differentiable and evaluated an order of magnitude faster than analytical approaches. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-world ANYmal quadruped. Our results demonstrate that this approach is capable of generating smooth and realisable trajectories. To the best of our knowledge, this is the first time latent space control has been successfully applied to a complex, real robot platform.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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