ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for
  Long-Horizon Tasks
Language-guided long-horizon mobile manipulation has long been a grand
challenge in embodied semantic reasoning, generalizable manipulation, and
adaptive locomotion. Three fundamental limitations hinder progress: First,
although large language models have improved spatial reasoning and task
planning through semantic priors, existing implementations remain confined to
tabletop scenarios, failing to address the constrained perception and limited
actuation ranges of mobile platforms. Second, current manipulation strategies
exhibit insufficient generalization when confronted with the diverse object
configurations encountered in open-world environments. Third, while crucial for
practical deployment, the dual requirement of maintaining high platform
maneuverability alongside precise end-effector control in unstructured settings
remains understudied.
  In this work, we present ODYSSEY, a unified mobile manipulation framework for
agile quadruped robots equipped with manipulators, which seamlessly integrates
high-level task planning with low-level whole-body control. To address the
challenge of egocentric perception in language-conditioned tasks, we introduce
a hierarchical planner powered by a vision-language model, enabling
long-horizon instruction decomposition and precise action execution. At the
control level, our novel whole-body policy achieves robust coordination across
challenging terrains. We further present the first benchmark for long-horizon
mobile manipulation, evaluating diverse indoor and outdoor scenarios. Through
successful sim-to-real transfer, we demonstrate the system's generalization and
robustness in real-world deployments, underscoring the practicality of legged
manipulators in unstructured environments. Our work advances the feasibility of
generalized robotic assistants capable of complex, dynamic tasks. Our project
page: https://kaijwang.github.io/odyssey.github.io/
68ac10cd86b21a0e2e358b8a
Kaijun Wang
claimed_verified
2025-08-25T08:18:39.940Z
664497116ed556326833b214
/avatars/729db0cd64c77030a5d8b2749fce084b.svg
Kaijun Wang
user
Ka12un
68ac10cd86b21a0e2e358b8b
Liqin Lu
68ac10cd86b21a0e2e358b8c
Mingyu Liu
claimed_verified
2025-08-25T12:37:47.093Z
652e25d2e647b0ee0a024f26
/avatars/b5c65cf6c8d0ddc9b8ef0226e0295d56.svg
Mingyu Liu
user
MingyuLiu
68ac10cd86b21a0e2e358b8d
Jianuo Jiang
68ac10cd86b21a0e2e358b8e
Zeju Li
claimed_verified
2025-09-09T13:50:19.499Z
67d2bf4c94707d574c821076
/avatars/cf143ae7efd3b1b0e10004eb2da70737.svg
Zeju Li
user
dongqianyu99
68ac10cd86b21a0e2e358b8f
Bolin Zhang
68ac10cd86b21a0e2e358b90
Wancai Zheng
68ac10cd86b21a0e2e358b91
Xinyi Yu
68ac10cd86b21a0e2e358b92
Hao Chen
68ac10cd86b21a0e2e358b93
Chunhua Shen
ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for
  Long-Horizon Tasks
Language-guided long-horizon mobile manipulation has long been a grand
challenge in embodied semantic reasoning, generalizable manipulation, and
adaptive locomotion. Three fundamental limitations hinder progress: First,
although large language models have improved spatial reasoning and task
planning through semantic priors, existing implementations remain confined to
tabletop scenarios, failing to address the constrained perception and limited
actuation ranges of mobile platforms. Second, current manipulation strategies
exhibit insufficient generalization when confronted with the diverse object
configurations encountered in open-world environments. Third, while crucial for
practical deployment, the dual requirement of maintaining high platform
maneuverability alongside precise end-effector control in unstructured settings
remains understudied.
  In this work, we present ODYSSEY, a unified mobile manipulation framework for
agile quadruped robots equipped with manipulators, which seamlessly integrates
high-level task planning with low-level whole-body control. To address the
challenge of egocentric perception in language-conditioned tasks, we introduce
a hierarchical planner powered by a vision-language model, enabling
long-horizon instruction decomposition and precise action execution. At the
control level, our novel whole-body policy achieves robust coordination across
challenging terrains. We further present the first benchmark for long-horizon
mobile manipulation, evaluating diverse indoor and outdoor scenarios. Through
successful sim-to-real transfer, we demonstrate the system's generalization and
robustness in real-world deployments, underscoring the practicality of legged
manipulators in unstructured environments. Our work advances the feasibility of
generalized robotic assistants capable of complex, dynamic tasks. Our project
page: https://kaijwang.github.io/odyssey.github.io/
ODYSSEY is a unified mobile manipulation framework for quadruped robots that integrates high-level task planning with low-level whole-body control, addressing challenges in egocentric perception, generalization, and coordination in unstructured environments.
hierarchical planner vision-language model long-horizon instruction decomposition whole-body policy sim-to-real transfer benchmark for long-horizon mobile manipulation