Walk This Way: Imitation-free Reinforcement Learning of Flexibly-Constrained Walking Controllers 60
Bibliographic record
Abstract
Locomotion is fundamental to the repertoire of skills required of physics-based human-like characters. Control policies are most commonly developed using reinforcement learning (RL) and using reward functions based on imitation of motion capture data. In this work, we propose an imitation-free RL training pipeline for bipedal locomotion controllers, as achieved using a multistage learning curriculum. Our work makes several contributions. First, it introduces a minimal set of additional specifications so that imitation-free RL can learn a single policy capable of in-place turning, side-stepping, hopping, and one-step foot plants, in addition to forwards and backwards walking. Second, the method offers precise and flexible conditioning, with control over footstep locations and further optional control over footstep timing, and footstep orientation. Third, we demonstrate that this imitation-free RL pipeline works across a range of body morphologies. Last, we show that the use of a plasticity-preservation technique allows for significantly faster learning. Our results demonstrate the scalability and effectiveness of using imitation-free RL approaches to develop flexible and highly-directable locomotion policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".