Adaptive PD Gains for Energy-Conscious Control in Physical Human-Robot Interaction
Bibliographic record
Abstract
Safe physical human-robot interaction (pHRI) is an important area of research. The dominant control approach in this domain is the impedance control method. However, such control schemes may require force feedback when implemented explicitly, translating to a need for several external force sensors. Impedance control is also a coordinate-dependent scheme, meaning the direction of applied forces must be tracked. These drawbacks indicate the need for alternate control schemes that can ensure safe behavior during pHRI. An alternative to this is energy-based approaches. One such control method is the `energy tank' approach, which tries to limit the total energy of a robot using `virtual tanks'. Another energy-based control scheme is the `artificial potential field' method, where attractive artificial potential fields are calculated to track a reference position while simultaneously limiting the robot's total energy. However, such methods can become relatively complex to implement. Energy tank approaches use the idea of energy to maintain stability while dynamically changing impedance parameters. Potential field methods are also complex to implement, as they may require constant recalculation of potential fields. Practically, many robot developers still use Proportional-Derivative (PD) controllers as the mid-level controller of choice, to track reference trajectories. PD controllers have the advantage of being simple to implement and well-understood due to their extensive research and investigation. We hence propose an adaptive PD controller that can limit a robot's energy under any given limit to achieve safe pHRI, by extending these energy limitation concepts to PD controllers. The proportional and derivative gains of the controller change depending on the current energy of the robot. The proposed controller can limit both the kinetic and potential energy of the robot under any given limit, and the behavior of the controller can be shaped using various parameters. We construct a stability proof for the controller and obtain a condition to ensure the controller's stability. We tested the behavior and compliance of this controller on the TALOS Robot by PAL Robotics both in simulation and on the actual robot, verifying the expected safe and compliant behavior.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".