Ensuring safety in human-robot dialog — A cost-directed approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present an approach for detecting potentially unsafe commands in human-robot dialog, where a robotic system evaluates task cost in input commands to ask input-specific, directed questions to ensure safe task execution. The goal is to reduce risk, both to the robot and the environment, by asking context-appropriate questions. Given an input program, (i.e., a sequence of commands) the system evaluates a set of likely alternate programs along with their likelihood and cost, and these are given as input to a Decision Function to decide whether to execute the task or confirm the plan from the human partner. A process called token-risk grounding identifies the costly commands in the programs, and specifically asks the human user to clarify those commands. We evaluate our system in two simulated robot tasks, and also on-board the Willow Garage PR2 and TurtleBot robots in an indoor task setting. In both sets of evaluations, the results show that the system is able to identify specific commands that contribute to high task cost, and present users the option to either confirm or modify those commands. In addition to ensuring task safety, this results in an overall reduction in robot reprogramming time.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it