DREAM Architecture: a Developmental Approach to Open-Ended Learning in\n Robotics
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".