Robust and Adaptive Dexterous Manipulation With Vision-Based Learning From Multiple Demonstrations
Bibliographic record
Abstract
In this article, we propose a vision-based learning-from-demonstration framework for a seven-degree-of-freedom (7-DOF) robotic manipulator. This framework enables learning from multiple contact-free human-hand demonstrations to execute dexterous pick-and-place tasks. Conventional methods for collecting demonstration data involve manually and physically moving the robot. These methods can be cumbersome, lack dexterity, and be physically straining. We leverage MediaPipe software, dynamic time warping (DTW), and Gaussian mixture model/regression to capture and regress multiple dexterous and marker-less hand motions. The proposed approach results in a more comprehensive motion representation, simplifying multiple demonstrations, and mitigating the non-smoothness inherent in single demonstrations. A novel dynamic movement primitives (DMP) with a variance-based force coupling term are developed to adaptively assimilate human actions into trajectories executable in dynamic environments. By considering the estimated variance from demonstration data, the DMP parameters are automatically fine-tuned and associated with the nonlinear terms to adapt the trajectories. To compensate for unknown external disturbances, non-singular terminal sliding mode (NTSM) control is applied for precise trajectory tracking. Experimental studies demonstrate the performance and robustness of our framework in executing demonstrations, motion planning, and control for a pick-and-place task.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".