Safe Reinforcement Learning for Arm Manipulation with Constrained Markov Decision Process
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
In the world of human–robot coexistence, ensuring safe interactions is crucial. Traditional logic-based methods often lack the intuition required for robots, particularly in complex environments where these methods fail to account for all possible scenarios. Reinforcement learning has shown promise in robotics due to its superior adaptability over traditional logic. However, the exploratory nature of reinforcement learning can jeopardize safety. This paper addresses the challenges in planning trajectories for robotic arm manipulators in dynamic environments. In addition, this paper highlights the pitfalls of multiple reward compositions that are susceptible to reward hacking. A novel method with a simplified reward and constraint formulation is proposed. This enables the robot arm to avoid a nonstationary obstacle that never resets, enhancing operational safety. The proposed approach combines scalarized expected returns with a constrained Markov decision process through a Lagrange multiplier, resulting in better performance. The scalarization component uses the indicator cost function value, directly sampled from the replay buffer, as an additional scaling factor. This method is particularly effective in dynamic environments where conditions change continually, as opposed to approaches relying solely on the expected cost scaled by a Lagrange multiplier.
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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