Humanoid Robot Motion Planning Approaches: a Survey
Bibliographic record
Abstract
Abstract Humanoid robots are complex, dynamic systems. Any humanoid robotic application starts with determining a sequence of optimal paths to perform a given task in a known or unknown environment. This paper critically reviews and rates available literature on the three key areas of multi-level motion and task planning for humanoid robots. First is efficiency while navigating and manipulating objects in environments designed for humans. Here, the research has broadly been summarized as behavior cloning approaches. Second is robustness to perturbations and collisions caused by operation in dynamic and unpredictable environments. Here, the modeling approaches integrated into motion planning algorithms have been the focus of many researchers studying humanoid motion’s balance and dynamic stability aspects. Last is real-time performance, wherein the robot must adjust its motion based on the most recent sensory data to achieve the required degree of interaction and responsiveness. Here, the focus has been on the kinematic constraints imposed by the robot’s mechanical structure and joint movements. The iterative nature of solving constrained optimization problems, the computational complexity of forward and inverse kinematics, and the requirement to adjust to a rapidly changing environment all pose challenges to real-time performance. The study has identified current trends and, more importantly, research gaps while pointing to areas needing further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".