.png){kind=icon}
返回
Hierarchical generative modelling for autonomous robots
.png){kind=icon}
OA
AI
DOI：10.1038/s42256-023-00752-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Humans generate intricate whole-body motions by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control and approached the problem of autonomous task completion by hierarchical generative modelling with multi-level planning, emulating the deep temporal architecture of human motor control. We explored the temporal depth of nested timescales, where successive levels of a forward or generative model unfold, for example, object delivery requires both global planning and local coordination of limb movements. This separation of temporal scales suggests the advantage of hierarchically organizing the global planning and local control of individual limbs. We validated our proposed formulation extensively through physics simulation. Using a hierarchical generative model, we showcase that an embodied artificial intelligence system, a humanoid robot, can autonomously complete a complex task requiring a holistic use of locomotion, manipulation and grasping: the robot adeptly retrieves and transports a box, opens and walks through a door, kicks a football and exhibits robust performance even in the presence of body damage and ground irregularities. Our findings demonstrated the efficacy and feasibility of human-inspired motor control for an embodied artificial intelligence robot, highlighting the viability of the formulized hierarchical architecture for achieving autonomous completion of challenging goal-directed tasks. Human and animal motion planning works at various timescales to allow the completion of complex tasks. Inspired by this natural strategy, Yuan and colleagues present a hierarchical motion planning approach for robotics, using deep reinforcement learning and predictive proprioception.
Keyword:
ACTIVE INFERENCE
BEHAVIOR
MOVEMENT
期刊
IF:
23.9
论文数:
1.3K
被引数:
1.5W
机构
引用论文
Learning to generate articulated behavior through the bottom-up and the top-down interaction processes.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏
查看更多内容