  Instructing a robot to complete an everyday task within our homes has been a
long-standing challenge for robotics. While recent progress in
language-conditioned imitation learning and offline reinforcement learning has
demonstrated impressive performance across a wide range of tasks, they are
typically limited to short-horizon tasks -- not reflective of those a home
robot would be expected to complete. While existing architectures have the
potential to learn these desired behaviours, the lack of the necessary
long-horizon, multi-step datasets for real robotic systems poses a significant
challenge. To this end, we present the Long-Horizon Manipulation (LHManip)
dataset comprising 200 episodes, demonstrating 20 different manipulation tasks
via real robot teleoperation. The tasks entail multiple sub-tasks, including
grasping, pushing, stacking and throwing objects in highly cluttered
environments. Each task is paired with a natural language instruction and
multi-camera viewpoints for point-cloud or NeRF reconstruction. In total, the
dataset comprises 176,278 observation-action pairs which form part of the Open
X-Embodiment dataset. The full LHManip dataset is made publicly available at
https://github.com/fedeceola/LHManip.
