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.
668f4aba4a4f5339ce284751
Nikita Chernyadev
claimed_verified
2025-04-16T09:42:51.102Z
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Nikita Cherniadev
user
nc-dev
668f4aba4a4f5339ce284752
Nicholas Backshall
668f4aba4a4f5339ce284753
Xiao Ma
claimed_verified
2025-07-24T09:15:33.301Z
6478597d91398856110a6738
/avatars/c3bc61eb7554ac21946b424e1314f1a7.svg
Xiao Ma
user
yusufma555
668f4aba4a4f5339ce284754
Yunfan Lu
claimed_verified
2024-07-11T08:25:50.146Z
668f7520a550fa61cddb690a
/avatars/ebae53ee5330e285ac6a55155298e5d1.svg
Yunfan Lu
user
lyfkyle
668f4aba4a4f5339ce284755
Younggyo Seo
claimed_verified
2024-11-25T08:34:41.432Z
6744164fe61fe2cad3ce3dc3
/avatars/c9ffeb09f4a62bf99869b3481e0d60a3.svg
Younggyo Seo
user
younggyoseo
668f4aba4a4f5339ce284756
Stephen James
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.
BiGym is a benchmark for mobile robotic manipulation with 40 diverse tasks, supporting imitation and demo-driven reinforcement learning.
bi-manual demo-driven robotic manipulation imitation learning demo-driven reinforcement learning proprioceptive data RGB depth camera views