Modeling nonconvex workspace constraints from diverse demonstration sets for Constrained Manipulator Visual Servoing
Bibliographic record
Abstract
This paper presents a novel framework for solving the Constrained Manipulator Visual Servoing (CMVS) problem. Classical eye-in-hand visual servoing relies on a reference image to capture the end-effector positioning task, but non-convex workspace constraints (such as whole-arm collision and camera occlusion constraints) are not represented. An explicit CAD model of the workspace is typically required for collision avoidance and visibility planning algorithms. In our novel CMVS framework, during the reference image capture process, we leverage the user's kinesthetic and visual capabilities to obtain a set of qualitatively-diverse demonstrations that provide information about the robot's work environment. We investigate methods for identifying the topology of the feasible regions represented directly in the control space of the robot (i.e., image-space and joint-space). We use a combination of stochastic modeling and graphical methods to describe the feasible space, capturing both the inter-group and intra-group variations. Specifically, our method uses the inter-groups variations to build a map that describes the global connectivity of the space, while exploiting the intra-group variations to automatically derive the appropriate gains in the control law. For a given target object, we apply online Gaussian Mixture Regression to the relevant feasible space regions to provide an idealized trajectory for tracking in image-space and in joint-space. We illustrate the key advantages of our approach through a set of visual servoing experiments on a Barrett WAM 7-DOF manipulator with a Sony XC-HR70 camera.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".