BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark
<|reference_start|>BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark: 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.<|reference_end|>
@article{chernyadev2024bigym:,
    title={BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark},
    author={Nikita Chernyadev, Nicholas Backshall, Xiao Ma, Yunfan Lu, Younggyo
  Seo, Stephen James},
    journal={arXiv preprint arXiv:2407.07788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07788},
    primaryClass={cs.RO cs.AI cs.CV cs.LG}
}