Adaptive Multi-Task Human-Robot Interaction Based on Human Behavioral Intention
Bibliographic record
Abstract
Learning from demonstrations with Probabilistic Movement Primitives (ProMPs) has been widely used in robot skill learning, especially in human-robot collaboration. Although ProMP has been extended to multi-task situation inspired by Gaussian mixture model, it still treats each task independently. ProMP ignores the common scenario that robots conduct adaptive switching of collaborative task in order to align with the intantaneous change of human intention. To solve this problem, we proposed an alternate learning-based parameter estimation method and an empirical minimum variation-based decomposition strategy with projection points, combining with linear interpolation strategy for weights, based on a Gaussian mixture model framework. Alternate learning of weights and parameters in multi-task ProMP (MTProMP) allows robot to obtain a smooth composite trajectory planning which crosses expected viapoints. Decomposition strategy reflects how the desired via-point state is projected onto individual ProMP component, rendering the minimum total sum of deviations between each projection point with the respective prior. Linear interpolation is used to adjust the weights among sequential via-points automatically. The proposed method and strategy are successfully extended to multi-task interaction ProMPs (MTiProMP). With MTProMP and MTiProMP, robot can be applied to multiple tasks in industrial factories and collaborate with workers to switch from one task to another according to changing intentions of human. Classical viapoints trajectory planning experiments and human-robot collaboration experiment are performed on Sawyer robot. The results of experiments show that MTProMP and MTiProMP with the proposed method and strategy perform better.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".