Refactoring the Curiosity Rover's Sample Handling Architecture on Mars
Bibliographic record
Abstract
The Curiosity Mars rover sample handling hardware and software were architected assuming that end-to-end sampling operations would occur in a single rover position, from acquisition of a powdered sample with a scoop or drill, through to the cleaning out of all sample residue in the sample chain. However, after analysis of the first drilled samples in Yellowknife Bay, the science team wanted to iterate with additional experiments on Mars and in laboratories on Earth to better understand their results and increase the value of science returned. With the architecture as conceived, the time needed to do so was in direct competition with the exploration of other targets and satisfaction of success criteria during the prime mission. The science team desired the capability to “cache” the sample for future use while continuing progress towards mission objectives by driving away and maintaining use of the robotic arm for contact science. Allowing sample to move about freely in this state risked hardware damage, ending the ability to deliver sample using the nominal path. In this paper we present the approaches that were developed to repurpose some of the sampling hardware into a series of caches and catchments that reduced this hardware risk to a level acceptable during the prime mission. This approach presented new challenges for rover planners, who had to learn to command the robotic arm using new routines that were too complicated to manage without assistance. The rover planner Software Simulation (“SSim”) was updated to track the turret gravity vector and sample state, generating an execution error or breakpoint as constraints were violated. Sample from the Cumberland drill target was cached for over 9 months, facilitating a number of scientific discoveries. As data accumulated and the mission transitioned into extended operations, the cached sample capability evolved to significantly simplify operations and reduce overhead.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".