Agile and versatile bipedal robot tracking control through reinforcement learning
This work addresses the challenge of dynamic balance and agility in bipedal robots, which is incremental as it builds on existing methods like model-based IK solvers and reinforcement learning.
The paper tackled the problem of enabling bipedal robots to achieve agile and versatile movement by proposing a controller that uses a small neural network with reinforcement learning for trajectory tracking across various gaits. The result showed the robot could move between target footholds at varying distances and heights and maintain static balance without repeated stepping adjustments.
The remarkable athletic intelligence displayed by humans in complex dynamic movements such as dancing and gymnastics suggests that the balance mechanism in biological beings is decoupled from specific movement patterns. This decoupling allows for the execution of both learned and unlearned movements under certain constraints while maintaining balance through minor whole-body coordination. To replicate this balance ability and body agility, this paper proposes a versatile controller for bipedal robots. This controller achieves ankle and body trajectory tracking across a wide range of gaits using a single small-scale neural network, which is based on a model-based IK solver and reinforcement learning. We consider a single step as the smallest control unit and design a universally applicable control input form suitable for any single-step variation. Highly flexible gait control can be achieved by combining these minimal control units with high-level policy through our extensible control interface. To enhance the trajectory-tracking capability of our controller, we utilize a three-stage training curriculum. After training, the robot can move freely between target footholds at varying distances and heights. The robot can also maintain static balance without repeated stepping to adjust posture. Finally, we evaluate the tracking accuracy of our controller on various bipedal tasks, and the effectiveness of our control framework is verified in the simulation environment.