  Humanoid activities involving sequential contacts are crucial for complex
robotic interactions and operations in the real world and are traditionally
solved by model-based motion planning, which is time-consuming and often relies
on simplified dynamics models. Although model-free reinforcement learning (RL)
has become a powerful tool for versatile and robust whole-body humanoid
control, it still requires tedious task-specific tuning and state machine
design and suffers from long-horizon exploration issues in tasks involving
contact sequences. In this work, we propose WoCoCo (Whole-Body Control with
Sequential Contacts), a unified framework to learn whole-body humanoid control
with sequential contacts by naturally decomposing the tasks into separate
contact stages. Such decomposition facilitates simple and general policy
learning pipelines through task-agnostic reward and sim-to-real designs,
requiring only one or two task-related terms to be specified for each task. We
demonstrated that end-to-end RL-based controllers trained with WoCoCo enable
four challenging whole-body humanoid tasks involving diverse contact sequences
in the real world without any motion priors: 1) versatile parkour jumping, 2)
box loco-manipulation, 3) dynamic clap-and-tap dancing, and 4) cliffside
climbing. We further show that WoCoCo is a general framework beyond humanoid by
applying it in 22-DoF dinosaur robot loco-manipulation tasks.
