BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark

Nikita Cherniadev, Nicholas Backshall, Xiao Ma, Yunfan Lu, Younggyo Seo, Stephen James

BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark: 1 upvotes on Hugging Face Daily Papers, #13 of 14 papers on 2024-07-11. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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