A Behavior-Based Reinforcement Learning Approach to Control Walking Bipedal Robots Under Unknown Disturbances
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Bibliographic record
Abstract
A new approach is developed to control 3-D bipedal motion and balance under disturbance, called the behavior-based locomotion controller (BBLC). Bipedal walking is divided into various task motions and optional control behaviors, which are utilized by a behavior-based controller to generate new balancing strategies (i.e., combinations of behaviors resulting in the balance of the robot) that are more robust to unknown external disturbances. A reinforcement learning (RL) algorithm, namely Q-learning, is used to determine which behavior combinations result in new balancing strategies. The controller is implemented on ABL-BI, a 13-DOF bipedal robot. Three different disturbance cases are examined: a push, step, and slope disturbance. For each case, the BBLC is able to generate a new balancing strategy that increases the robustness of the system to the disturbance. The BBLC framework also provides the ability to interpret the RL agent’s actions, due to the combination of discrete behaviors that the agent deals with. Additionally, an evaluation of the selected balancing behaviors is completed using a stability analysis of the linear inverted pendulum.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it