DREAM Architecture: a Developmental Approach to Open-Ended Learning in\n Robotics
Why this work is in the frame
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Bibliographic record
Abstract
Robots are still limited to controlled conditions, that the robot designer\nknows with enough details to endow the robot with the appropriate models or\nbehaviors. Learning algorithms add some flexibility with the ability to\ndiscover the appropriate behavior given either some demonstrations or a reward\nto guide its exploration with a reinforcement learning algorithm. Reinforcement\nlearning algorithms rely on the definition of state and action spaces that\ndefine reachable behaviors. Their adaptation capability critically depends on\nthe representations of these spaces: small and discrete spaces result in fast\nlearning while large and continuous spaces are challenging and either require a\nlong training period or prevent the robot from converging to an appropriate\nbehavior. Beside the operational cycle of policy execution and the learning\ncycle, which works at a slower time scale to acquire new policies, we introduce\nthe redescription cycle, a third cycle working at an even slower time scale to\ngenerate or adapt the required representations to the robot, its environment\nand the task. We introduce the challenges raised by this cycle and we present\nDREAM (Deferred Restructuring of Experience in Autonomous Machines), a\ndevelopmental cognitive architecture to bootstrap this redescription process\nstage by stage, build new state representations with appropriate motivations,\nand transfer the acquired knowledge across domains or tasks or even across\nrobots. We describe results obtained so far with this approach and end up with\na discussion of the questions it raises in Neuroscience.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.000 | 0.002 |
| 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