Gene

Robot

Control (management)

Engineering

Ecology

Biology

Motion planning

Control engineering

Robustness (evolution)

Biochemistry

Computer science

Controller (irrigation)

Quadrupedalism

Motion control

Reinforcement learning

Simulation

Anatomy

Agronomy

Artificial intelligence

Medicine

Chemistry

Terrain

Control theory (sociology)

IEEE Transactions on Robotics

We present a unified model-based and data-driven approach for quadrupedal planning and control to achieve dynamic locomotion over uneven terrain. We utilize on-board proprioceptive and exteroceptive feedback to map sensory information and desired base velocity commands into footstep plans using a reinforcement learning (RL) policy. This RL policy is trained in simulation over a wide range of procedurally generated terrains. When ran online, the system tracks the generated footstep plans using a model-based motion controller. We evaluate the robustness of our method over a wide variety of complex terrains. It exhibits behaviors which prioritize stability over aggressive locomotion. Additionally, we introduce two ancillary RL policies for corrective whole-body motion tracking and recovery control. These policies account for changes in physical parameters and external perturbations. We train and evaluate our framework on a complex quadrupedal system, ANYmal version B, and demonstrate transferability to a larger and heavier robot, ANYmal C, without requiring retraining.

RLOC: Terrain-Aware Legged Locomotion Using Reinforcement Learning and Optimal Control