Goal-directed robot manipulation through axiomatic scene estimation
Bibliographic record
Abstract
Performing robust goal-directed manipulation tasks remains a crucial challenge for autonomous robots. In an ideal case, shared autonomous control of manipulators would allow human users to specify their intent as a goal state and have the robot reason over the actions and motions to achieve this goal. However, realizing this goal remains elusive due to the problem of perceiving the robot’s environment. We address and describe the problem of axiomatic scene estimation for robot manipulation in cluttered scenes which is the estimation of a tree-structured scene graph describing the configuration of objects observed from robot sensing. We propose generative approaches to scene inference (as the axiomatic particle filter, and the axiomatic scene estimation by Markov chain Monte Carlo based sampler) of the robot’s environment as a scene graph. The result from AxScEs estimation are axioms amenable to goal-directed manipulation through symbolic inference for task planning and collision-free motion planning and execution. We demonstrate the results for goal-directed manipulation of multi-object scenes by a PR2 robot.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".