Deep Reinforcement Learning Enables Joint Trajectory and Communication in Internet of Robotic Things
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Bibliographic record
Abstract
Internet of Robotic Things (IoRT) emphasizes the integrated robotic, artificial intelligence computing, and communication technologies, enabling more sophisticated operations and decision-making. As a crucial element of IoRT, mission-critical applications, such as industrial manufacturing and emergency services, impose stringent requirements on ultra-reliable and low-latency communication (URLLC). The paper focuses on addressing URLLC challenges in the context of IoRT, particularly when autonomous mobile robots (AMRs) coexist with static sensors. We prioritize safe and efficient AMRs’ travel through trajectory design and communication resource allocation in IoRT systems without the need of any prior knowledge. To enhance network connectivity and exploit diversity gains, we introduce the flexible decoding and free clustering as the next-generation multiple access technologies in spectrum-limited downlink IoRT system. Then, aiming at minimizing the decoding error probability and travel time, we formulate a long-term multi-objective optimization problem by jointly designing AMRs’ trajectory and communication resource. To accommodate the inherent dynamics and unpredictability in the IoRT system, we introduce a multi-agent actor-critic deep reinforcement learning (DRL) framework, offering four distinct implementations, each accompanied by comprehensive complexity analyses. Simulation results reveal the following insights: 1) in terms of DRL implementations, off-policy algorithms with deterministic policies outperform their on-policy counterparts, achieving approximately a 67% increase in rewards; 2) In terms of communication schemes, our proposed flexible decoding and free clustering strategies under designed trajectories can effectively reduce decoding errors; and 3) In terms of algorithm optimality, our DRL framework shows superior flexibility and adaptability in communication environments compared to traditional A* search and heuristic methods.
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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 | 0.001 |
| 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