Learning-driven sensorless interaction force estimation for low-cost robot arm with limited dynamic features
Bibliographic record
Abstract
Precise measurement of the interaction force between the robot and its environment benefits the decision-making processes in various robotic applications. Compared with sensor-based methods, sensorless approaches are commonly preferred due to their versatility and cost-effectiveness. This paper introduces a learning-based method that leverages the state-of-the-art transformer to accurately estimate the interaction force. In contrast to other estimation methods relying on accurate robot dynamic parameters, state information or image features, a notable innovation of our work is the utilization of the limited set of features. The elaborate feature set only includes the joint angle, velocity, and driven torque, with the omission of joint acceleration—a basic robot state typically employed in other research. This configuration expands the feasibility of the presented approach to low-cost robots which are solely equipped with encoders in each joint, and to scenarios where the collection of clear and unobstructed visual features are challenging. Another distinctive feature of our work is that both soft and stiff objects during interaction are considered. Results from the experiment demonstrate that, in comparison to previous image-based methods, our framework achieves an equivalent or even superior level of accuracy across a broader spectrum of environments. Additionally, due to the elimination of joint acceleration from the feature set, the proposed framework sacrifices a small degree of accuracy compared with some non-image-based methods to broaden its applicability.
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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 | 0.000 |
| 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 | 0.001 |
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".