我们最近在《Biomimetics》期刊上发表的论文引入了一种统一的机器人方法，通过强化学习和过度约束的机器臂将运动和操作技能融合在一起，揭示了这些关键能力的可转移性。研究表明，通过重新设计机器人硬件并采用创新的学习技术，机器人可以在运动和操作任务之间无缝切换，这标志着该领域的重大进展。
Our recent paper published on Biomimetics introduces a unified approach to robotics, merging locomotion and manipulation skills through reinforcement learning and overconstrained robotic limbs, uncovering the transferability of these crucial capabilities. The study reveals that by redesigning robotic hardware and employing innovative learning techniques, robots can seamlessly transition between locomotion and manipulation tasks, marking a significant advancement in the field.
doi: https://doi.org/10.3390/biomimetics8040364
最近，我们在仿生学领域发表了一篇新的研究论文，通过利用强化学习（RL）和一种新型的过约束机器人肢体，探索了机器人的运动和操作的统一性。这一创新性方法不仅弥合了运动和操作之间的鸿沟，还揭示了这两个机器人行为的关键方面之间的技能可迁移性。
Recently, we published a new research paper on Biomimetics to explore a unified formulation of locomotion and manipulation in robotics, leveraging reinforcement learning (RL) and a unique design with overconstrained robotic limbs. This innovative approach not only bridges the gap between locomotion and manipulation but also reveals the transferability of skills between these two essential aspects of robotic behavior.
运动与操作的难题｜Locomotion and Manipulation Conundrum
运动和操作是机器人领域的基本技能，但传统上它们被视为不同的问题。然而，我们的研究揭示了一种寻求统一这两个看似分开的任务的新方法，展示了多足运动和多指操作之间共享的内在模型，并旨在为进一步研究提供数据驱动的证据。
Locomotion and manipulation are fundamental skills in the field of robotics, but traditionally, they have been treated as distinct problems. However, our research unveils a new approach that seeks to unify these two seemingly separate tasks. The study demonstrates the intrinsic model shared between multi-legged locomotion and multi-fingered manipulation and aims to provide data-driven evidence for further research in this direction.
过约束机器人肢体｜The Overconstrained Robotic Limbs
我们重新配置了具有过度约束设计的机器臂，可以适应多种形式，包括多足和多指机器人。这一创新提供了一个多功能且可共享的平台，用于强化运动和操作学习。通过启用重新配置，团队采用了一种协同培训方法，为统一的运动和操作政策铺平了道路。
We have reconfigured robotic limbs with an overconstrained design that can be adapted into various forms, including multi-legged and multi-fingered robots. This innovation offers a versatile and shareable platform for reinforcement loco-manipulation learning. By enabling reconfiguration, the team adopted a co-training approach for reinforcement learning, paving the way for a unified loco-manipulation policy.
发现「行走-操作」技能的可迁移性｜Transferability Discovered
通过广泛的实验，我们发现了强有力的数据驱动证据，支持了运动和操作之间技能可转移性的存在。这是通过使用单一的RL政策和多层感知器（MLP）或图神经网络（GNN）之一来实现的。这些发现代表了机器人系统更加多才多艺和适应性发展的重要一步。
Through extensive experimentation, we found compelling data-driven evidence to support the transferability of skills between locomotion and manipulation. This was accomplished using a single RL policy with either a Multi-Layered Perceptron (MLP) or a Graph Neural Network (GNN). The findings represent a significant step forward in the development of more versatile and adaptive robotic systems.
硬件设计至关重要｜Hardware Design Matters
我们还揭示了机器人硬件配置显著影响了转移学习。值得注意的是，水平和垂直配置之间的成功率和可转移性存在差异。水平机器人表现出更好的可转移性，而垂直配置表现出更低的收敛速度。结果表明，硬件设计在增强运动和操作技能的互换性方面起到了关键作用。
We also revealed that the configuration of the robotic hardware significantly influences transfer learning. Notably, the success rates and transferability differ between horizontal and vertical configurations. Horizontal robots demonstrated better transferability, whereas vertical configurations exhibited lower convergence rates. The results suggest that hardware design plays a pivotal role in enhancing the interchangeability of locomotion and manipulation skills.
「行走-操作」行为的继承｜Behavioral Inheritance
该研究探讨了迁移学习的行为方面。结果显示，在运动任务中，一些操作技能会继承，而在从运动转移到操作的操作任务中也会出现这种继承。这一发现突显了在机器人学的这两个关键方面的技能转移的复杂性。
The study delves into the behavioral aspects of transfer learning. The results show that some manipulation skills are inherited in locomotion tasks, and this inheritance is reciprocated in the manipulation tasks transferred from locomotion. This insight highlights the intricacies of skill transfer between these two critical facets of robotics.
从仿真到实际的迁移｜Sim-to-Real Transfer
该研究的一个显著成就是成功演示了从仿真到实际的技能可转移性。我们将从仿真中获得的关节位置命令重新播放到物理机器人中，展示了在实际情况中应用所学技能的潜力。这一发展对于实际应用机器人系统具有重要影响。
One notable achievement of the research is the successful demonstration of sim-to-real transferability. We replayed joint position commands from simulations to physical robots, showcasing the potential for deploying learned skills in real-world scenarios. This development has substantial implications for the practical application of robotic systems.
未来方向｜Future Directions
虽然这项研究在该领域迈出了重要一步，但我们承认还有更多的工作要做。未来的努力将包括扩大运动和操作任务的范围，并研究更复杂的运动和物体之间的技能可转移性。此外，集成感知信息，如触觉感知和地面反作用力传感器，对于处理不同地形和物体至关重要。
While this research is a significant leap forward in the field, we acknowledges that there is more work to be done. Future endeavors include expanding the range of locomotion and manipulation tasks and investigating the transferability between more complex movements and objects. Additionally, the integration of sensory information, such as tactile sensing and ground reaction force sensors, will be crucial for handling diverse terrains and objects.
总之，这项研究为机器人领域开辟了新的激动人心的途径。通过强化学习和创新的硬件设计将运动和操作统一起来，机器人即将变得更加多才多艺和适应性，能够在各种任务之间无缝过渡。这项工作代表了朝着更智能和多才多艺的机器人的发展迈出的关键一步，它们可以以精确和高效的方式导航和操作环境。
In conclusion, this research opens exciting new avenues for the world of robotics. By unifying locomotion and manipulation through reinforcement learning and innovative hardware design, robots are on the cusp of becoming more versatile and adaptable, capable of seamlessly transitioning between a wide range of tasks. This work represents a pivotal step toward the development of more intelligent and versatile robots that can navigate and manipulate their environments with precision and efficiency.
Haoran Sun#, Linhan Yang#, Yuping Gu, Jia Pan*, Fang Wan*, and Chaoyang Song* (2023). “Bridging Locomotion and Manipulation Using Reconfigurable Robotic Limbs via Reinforcement Learning.” Biomimetics, 8(4), 364.
doi: https://doi.org/10.3390/biomimetics8040364
