Mobile manipulator planning under uncertainty in unknown environments
Bibliographic record
Abstract
We present a sampling-based mobile manipulator planner that considers the base pose uncertainty and the effects of this uncertainty on manipulator motions. The overall planner has three distinct and novel features: (i) it uses the Hierarchical and Adaptive Mobile Manipulator Planner (HAMP) that plans for both the base and the arm in a judicious manner; (ii) it uses localization-aware sampling and connection strategies to consider only those nodes and edges which contribute toward better localization; (iii) it incorporates base pose uncertainty along the edges (where arm remains static) and the effects of this uncertainty are considered on arm motion. We call this overall planner HAMP-BUA, where BUA denotes “Base pose Uncertainty and its propagation to Arm motions.” First we evaluate our planner in known static environments and show that it finds a safer path as compared with other variants where uncertainty is not considered at different levels as mentioned above. Next, we incorporate our planner within an integrated and fully autonomous system for mobile pick-and-place tasks in unknown static environments. A key aspect of our integrated system is that the planner works in tandem with base and arm exploration modules that explore the unknown environment. Our system is implemented both in simulation and on the actual Simon Fraser University (SFU) mobile manipulator and we present the corresponding results. It demonstrates a level of competency in exploring unknown environments for carrying out pick-and-place tasks that has not been demonstrated previously.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".