IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2606. 08729v1 Announce Type: cross Abstract: Simulation plays a key role in automated robotics research supported by large language models (LLMs).
SimSkill is a lifelong learning AI agent that uses the SUMO traffic simulator to autonomously identify gaps in its capabilities, generate and solve tasks grounded in the environment, and verify solutions through an action‑critic loop. It consolidates experience into episodic, procedural, and semantic memory without updating its backbone language model, creating a reusable library for traffic‑simulation workflows. Evaluations on two benchmarks with three different LLM backbones show that SimSkill can improve verified completion rates by up to 25 percentage points, with procedural and semantic memory contributing complementarily to performance.
arXiv:2607. 10991v1 Announce Type: cross Abstract: As mobile robots become more integrated into everyday human environments, social robot navigation is becoming essential for ensuring human comfort, safety, and trust.
arXiv:2606. 01478v1 Announce Type: cross Abstract: High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms.
arXiv:2608. 14944v1 Announce Type: cross Abstract: Natural-language interfaces can lower the barrier to programming robots, but existing systems struggle when users request complex tasks.
arXiv:2609.39915v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires embodied agents to generate actions based on instructions and observations. General-purpose multimodal agents...