  We introduce BiGym, a new benchmark and learning environment for mobile
bi-manual demo-driven robotic manipulation. BiGym features 40 diverse tasks set
in home environments, ranging from simple target reaching to complex kitchen
cleaning. To capture the real-world performance accurately, we provide
human-collected demonstrations for each task, reflecting the diverse modalities
found in real-world robot trajectories. BiGym supports a variety of
observations, including proprioceptive data and visual inputs such as RGB, and
depth from 3 camera views. To validate the usability of BiGym, we thoroughly
benchmark the state-of-the-art imitation learning algorithms and demo-driven
reinforcement learning algorithms within the environment and discuss the future
opportunities.
