Obstacle-free Trajectory Planning of an Uncertain Space Manipulator: Learning from a Fixed-Based Manipulator
Bibliographic record
Abstract
In a typical space debris mitigation mission, a space manipulator must plan maneuvers free of self-collision and collision with the noncooperative target satellite to perform a safe capture. We develop an effective model-free planner based on deep reinforcement learning for free-floating manipulators that only relies on an uncertain target position feedback. At its core, the learning agent employs the Deep Deterministic Policy Gradient (DDPG) algorithm capable of working with continuous states and actions. To improve the learning performance, we propose a five-step sequential learning that uses priority episode sampling to effectively transfer knowledge from a fixed-based manipulator trained to follow a moving target to an uncertain space manipulator capturing a target point on a satellite. Further, to avoid moving obstacles, we introduce the notion of multi-critic in the DDPG setting, such that one critic optimizes the task of chasing in an uncertain environment and another one focuses on obstacle avoidance. To show the efficacy of the developed trajectory planner, we compare its running average success rate and reward value with a baseline DDPG.
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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 | 0.002 |
| 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".