Autonomous Soft-Tissue Needle Steering Using Reinforcement Learning Guided by Human Input
Bibliographic record
Abstract
Soft-tissue needle steering, where a deformable needle is inserted into the tissue to guide its tip to a desired position, is a common minimally invasive surgery (MIS) procedure. The diverse types of needles and complex tissue dynamics limit the use of existing approaches that utilize models of the needle and the tissue for automating the task. In this work, we employ a data-driven approach using deep reinforcement learning (DRL) to achieve autonomous needle steering by viewing it as a multi-goal reinforcement learning problem. Human interventions are incorporated during training to accelerate learning and reduce catastrophic failures. Generative adversarial imitation learning (GAIL) is combined with regular DRL by utilizing a hindsight relabeling scheme for human interventions to encourage the agent to imitate human behavior. To emulate the sim-to-real process, an agent is first trained in a simplistic simulation environment for needle steering and then transferred to a sophisticated one considered as the real world with fine-tuning (sim-to-sim). Experimental results show that with human interventions, the proposed method outperforms the other compared DRL approaches and can achieve good performance with only 2,000 training steps in the complex simulation environment, achieving an average return comparable to that of a 55,000-step agent trained from scratch.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".