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#sim-to-real

3 curated events
papersTODAY 04:00 UTC

Multi-Agent RL Approach to Factory Task Assignment and Navigation Tested on Real Robots

A new arXiv paper examines how multi-agent reinforcement learning can be applied to task assignment and navigation for robot fleets in industrial settings. The authors focus on the gap between simulation training and deployment on physical multi-robot systems, a step that remains difficult in practice. The work reports on transferring learned policies from simulated environments to real factory robots.

reinforcement-learning/robotics/multi-agent/sim-to-real
papersTODAY 04:00 UTC

Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Assembly

The paper introduces a pretraining method that uses proprioceptive signals as an anchor to align different sensory modalities, aiming to help robots handle contact-rich assembly tasks. Such tasks demand submillimeter precision and accurate force interpretation during sustained contact, which makes sim-to-real transfer difficult. The approach targets zero-shot deployment of policies trained in simulation onto real hardware. This is a replacement listing on arXiv cs.AI.

robotics/sim-to-real/pretraining/manipulation
papersSEP 10 04:00 UTC

Sim2Signal: Sim-to-Real Benchmarks for RL-Based Traffic Signal Control

A new arXiv paper introduces Sim2Signal, a benchmark suite for measuring how reinforcement learning policies for traffic signal control transfer from simulators to real-world deployment. The work targets the sim-to-real gap, where agents that perform well in simulation often fail once applied in practice. The benchmarks aim to give researchers a standardized way to evaluate transferability before deployment.

reinforcement-learning/benchmarks/traffic/sim-to-real