  Electric quadruped robots used in outdoor exploration are susceptible to
leg-related electrical or mechanical failures. Unexpected joint power loss and
joint locking can immediately pose a falling threat. Typically, controllers
lack the capability to actively sense the condition of their own joints and
take proactive actions. Maintaining the original motion patterns could lead to
disastrous consequences, as the controller may produce irrational output within
a short period of time, further creating the risk of serious physical injuries.
This paper presents a hierarchical fault-tolerant control scheme employing a
multi-task training architecture capable of actively perceiving and overcoming
two types of leg joint faults. The architecture simultaneously trains three
joint task policies for health, power loss, and locking scenarios in parallel,
introducing a symmetric reflection initialization technique to ensure rapid and
stable gait skill transformations. Experiments demonstrate that the control
scheme is robust in unexpected scenarios where a single leg experiences
concurrent joint faults in two joints. Furthermore, the policy retains the
robot's planar mobility, enabling rough velocity tracking. Finally, zero-shot
Sim2Real transfer is achieved on the real-world SOLO8 robot, countering both
electrical and mechanical failures.
