title: BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark
summary: 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_authors._id: 668f4aba4a4f5339ce284751
paper_authors.name: Nikita Chernyadev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T09:42:51.102Z
paper_authors._id: 669d64ad3197305664cbea29
paper_authors.avatarUrl: /avatars/dd5a7475e327c037def68d17e08938e9.svg
paper_authors.fullname: Nikita Cherniadev
paper_authors.type: user
paper_authors.user: nc-dev
paper_authors._id: 668f4aba4a4f5339ce284752
paper_authors.name: Nicholas Backshall
paper_authors._id: 668f4aba4a4f5339ce284753
paper_authors.name: Xiao Ma
paper_authors._id: 668f4aba4a4f5339ce284754
paper_authors.name: Yunfan Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-11T08:25:50.146Z
paper_authors._id: 668f7520a550fa61cddb690a
paper_authors.avatarUrl: /avatars/ebae53ee5330e285ac6a55155298e5d1.svg
paper_authors.fullname: Yunfan Lu
paper_authors.type: user
paper_authors.user: lyfkyle
paper_authors._id: 668f4aba4a4f5339ce284755
paper_authors.name: Younggyo Seo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-25T08:34:41.432Z
paper_authors._id: 6744164fe61fe2cad3ce3dc3
paper_authors.avatarUrl: /avatars/c9ffeb09f4a62bf99869b3481e0d60a3.svg
paper_authors.fullname: Younggyo Seo
paper_authors.type: user
paper_authors.user: younggyoseo
paper_authors._id: 668f4aba4a4f5339ce284756
paper_authors.name: Stephen James
paper_title: BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark
paper_summary: 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_ai_summary: BiGym is a benchmark for mobile robotic manipulation with 40 diverse tasks, supporting imitation and demo-driven reinforcement learning.
paper_ai_keywords: bi-manual demo-driven robotic manipulation imitation learning demo-driven reinforcement learning proprioceptive data RGB depth camera views