Humanoid locomotion in confined spaces requires continuous height adaptation across multiple locomotion modes, yet most existing approaches remain limited to height-adjustable bipedal walking. We present REACH, an RL-based hierarchical framework that enables a Unitree G1 humanoid to traverse height-constrained environments through variable-height bipedal walking and crawling. The learned policies regulate body height over complementary command ranges of 1.27--0.50m in bipedal locomotion and 0.50--0.20m in crawling, extending traversal beyond the clearance limits of crouched walking. To address contact-rich crawling dynamics, we employ a history-conditioned latent representation that captures terrain and interaction context from proprioceptive observations. A bidirectional transition policy further connects crouched bipedal locomotion and crawling across large kinematic and contact discontinuities. These skills are integrated with a LiDAR-based planner that converts point clouds into collision-free three-dimensional paths and locomotion commands, including proactive height targets. Real-world experiments demonstrate accurate height and velocity tracking, robust terrain traversal, reliable mode transitions and confined-space traversal with the proposed pipeline. Code and videos are available at https://aislab-sustech.github.io/REACH/.
Figures & tables
Fig. 1: We introduce REACH, a modular, adaptable, RL-based hierarchical framework for humanoid crawling, enabling the Unitree G1 to transition smoothly between bipedal walking and crawling while traversing confined spaces.
Fig. 2: Overview of REACH. (A) A bidirectional motion-tracking policy connects crouched bipedal locomotion and crawling. (B) The history-conditioned asymmetric actor–critic encodes proprioceptive histories, predicts future observations and body velocity, and uses privileged information only for critic training. (C) A model-based planner converts LiDAR point clouds into locomotion and height commands. (D) Real-world trials demonstrate crawling through consecutive low-clearance obstacles.
Fig. 3: Real-world evaluation of the complementary locomotion capabilities enabled by REACH. (A) Variable-height bipedal walking continuously adjusts body height to traverse an 80 cm-clearance obstacle. (B) Quadrupedal crawling enables robust multi-contact traversal through passages as low as 40 cm. (C) Bidirectional transitions between bipedal standing and quadrupedal crawling enable the robot to enter and exit low-clearance obstacles under different locomotion modes.
Variant
Hist. estimator
Priv. scan
Idle penalty
Full
✓
–
✓
No-Idle
✓
–
–
Proprio
–
–
✓
Priv-Scan
–
✓
✓
TABLE I: Ablation variants for different locomotion modes.
Fig. 4: Low-level locomotion ablations. (a,b) Mean terrain-curriculum level over training iterations. (c,d) Absolute command-tracking errors (median, IQR, 10th–90th percentiles; descriptive, not confidence intervals). † : crawling variants with no curriculum progress—low yaw-rate error should not be read as successful locomotion.
Fig. 5: Bidirectional transition success over 25 trials per direction. The first three configurations are evaluated in MuJoCo; the hatched Full (Real) bars denote deployment of the unchanged full policy on the Unitree G1.