Active sensing for motion planning in uncertain environments via mutual information policies
Bibliographic record
Abstract
This paper addresses path planning with real-time reaction to environmental uncertainty. The environment is represented as a robotic roadmap, or graph, and is uncertain in that the edges of the graph are unknown to the robot a priori. Instead, the robot’s prior information consists of a distribution over candidate edge sets, modeling the likelihood of certain obstacles in the environment. The robot can locally sense the environment, and at a vertex, can determine the presence or absence of some subset of edges. Within this model, the reactive planning problem provides the robot with a start location and a goal location and asks it to compute a policy that minimizes the expected travel and observation cost. In contrast to computing paths that maximize the probability of success, we focus on complete policies (i.e., policies that are guaranteed to navigate the robot to the goal or determine no such path exists). We prove that the problem is NP-hard and provide a suboptimal, but computationally efficient solution. This solution, based on mutual information, returns a complete policy and a bound on the gap between the policy’s expected cost and the optimal. We test the performance of the policy and the lower bound against that of the optimal policy and explore the effects of errors in the robot’s prior information on performance. Simulations are run on a flexible factory scenario to demonstrate the scalability of the proposed approach. Finally, we present a method to extend this solution to robots with faulty sensors.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".