Task Space Bilateral Teleoperation of Co-manipulators using Power-based TDPC and Leader-follower Admittance Control
Bibliographic record
Abstract
In this paper, the bilateral teleoperation of cooperative manipulators is achieved and experimentally analyzed. The master and slave robots are asymmetrical, and only the master end effector’s task space velocity signals are transmitted through the communication network, while the task space force signals of slave robot are relayed back. A power-based time domain passivity control (PTDPC) approach is employed for the controller design to ensure the passivity of the communication channel in the presence of time-varying delays and are applied to each side of the communication channel at every time constant. This model-free method does not require the dynamic models of the master or slave systems to be known. The slave robot acts as the leader of the remote dual-arm cooperative manipulator system that is used to manipulate a common rigid object. This leader robot is controlled using position control mode to track the trajectory of the master, while the follower robot employs an admittance control method to follow the leader’s motion trend. The follower robot is not required to transmit or receive any communication data, which simplifies the network communication topology. Experimental results are presented to verify the effectiveness and simplicity of the designed framework in the presence of large, time-varying and asymmetric delays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".