Organizations: Noetix Robotics, Beijing, China. · the University of Science and Technology of China (USTC), Hefei, China. · [Institution Name], [City], [Country]. · the National University of Defense Technology (NUDT), Changsha, China.
We present a progressive imitation framework that learns terrain-appropriate humanoid parkour skills and subsequently adapts their hand--knee support. Human demonstrations provide traversal behaviors, but their contact patterns need not suit a robot's proportions or the available support surfaces. Our framework changes the role of demonstrations across three stages: privileged imitation produces physically executable robot references; terrain-indexed adversarial motion priors guide skill acquisition; and geometric support guidance with a reduced motion-prior weight enables contact adaptation. Candidate-based precontact attraction and actual-contact evaluation guide support without prescribing a contact sequence. The deployed policy selects behaviors from onboard depth, proprioception, and velocity commands without an external skill label. On the Noetix E1, the policy achieves 96.34% hybrid success in simulation and transfers without real-world policy fine-tuning, completing 29 of 30 individual obstacle trials and four of five hybrid courses. Support-adaptation ablations show higher traversal success for the complete adaptation stage on the tested terrains, while qualitative observations show changes in contact placement. These results support acquiring skills from demonstrations before relaxing their influence to accommodate robot-specific support configurations.
Figures & tables
Fig. 2: Overview of the training framework. Motion preprocessing (right) retargets human demonstrations and physically refines them into robot reference rollouts with kinematic, height-scan, and dynamic information. Terrain-indexed adversarial supervision compares reference and policy motion windows to provide a routed style reward. Geometric hand–knee candidates (bottom) guide precontact placement, while contact evaluation and state transitions determine support and retention terms. Task, style, and support rewards are combined with the weights in Eq. 1 for actor–critic training. The actor and critic use gated expert networks, distinct from the discriminator heads. At deployment, only the actor and its observation-processing modules are retained; privileged geometry, reference motions, discriminators, and the critic remain in training.
Fig. 3: Support Candidate mechanism (schematic, not to scale). (a) The frame’s open center is excluded from support surfaces. (b) Footprint compatibility and approximate 3-D ellipsoidal reachability filter candidate sets; the dashed ellipse illustrates a projection. Filled circles with rectangles denote valid candidate footprints, a cross denotes edge rejection, and an open circle denotes an unreachable candidate. (c) Predicted endpoints receive weighted attraction to valid candidates. (d) Actual contact quality is queried at the measured endpoint, with contact, candidate-availability and phase gates. The requested band width is 0.55 m; the native horizontal top is approximately 0.45 m wide.
Parameter
Value
Parallel environments / rollout
4,096 / 24 control steps
PPO epochs / minibatches
5 / 4
PPO learning rate / target KL
10−3 adaptive / 0.01
Clip / discount / GAE
0.2 / 0.99 / 0.95
Entropy / value loss / max. gradient
0.01 / 1 / 1
Discriminator learning rate
5×10−6
TABLE II: Training configuration shared by the formal comparisons.
Method
Low
Mid
High
Hybrid
OOD
Ours
100
98.44
98.14
96.34
93.46
w/o Stage 2
41.36
21.14
13.87
0
0
w/o Stage 3
100
85.55
79.69
83.59
85.11
Velocity Tracking
96.09
0
0
0
0
Single AMP
100
65.82
0
0
44.29
Multi-Skill Distillation
100
99.28
95.10
79.65
65.33
TABLE III: Complete-traversal success (%): 2,048 episodes per condition. Low/Mid/High follow Table I . Hybrid requires all three obstacles without resets; OOD heights lie 1–3 cm outside the corresponding training ranges.
Fig. 4: Supplementary evidence for terrain–skill correspondence. Top: a simulation montage of sequential obstacle traversal. Middle: joint-angle DTW distances (rad) to the High, Mid, and Low references. Bottom: lowest-distance intervals, with the dominant sequence High → Mid → Low. The interval labels identify reference matches rather than independently measured terrain boundaries.
Fig. 5: Qualitative support comparison. Top: w/o Stage 3; bottom: Ours. Each row combines a simulation contact close-up (left) and hardware traversal snapshots (right). Red circles in the upper row highlight hand-contact regions near the platform edge. The comparison illustrates support adaptation rather than a measured contact-force or stability margin.
Method
Low
Mid
High
1m×1m Top
Ours
100
98.44
98.14
93.46
w/o Candidates
99.17
83.15
71.53
46.24
w/o Relaxation
99.56
85.40
83.20
58.89
w/o Stage 3
100
85.55
79.69
50.63
TABLE IV: Success rates for support-adaptation ablations (%).
Fig. 6: Supplementary real-world hybrid traversal sequence. Snapshots are ordered from left to right, top to bottom, with timestamps relative to t0 . The two rows show different obstacle-crossing maneuvers within the same trial, illustrating terrain-dependent skill switching by a single perception-conditioned policy without external skill-selection commands.