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...
arXiv:2606.03963v4 Announce Type: replace-cross Abstract: Deep reinforcement learning enables autonomous robots to learn complex navigation tasks, but still relies heavily on time consuming manual re...
arXiv:2606. 08610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pipeline surrounding the algorithms.
arXiv:2606. 03963v2 Announce Type: replace-cross Abstract: Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks.
arXiv:2505. 01458v2 Announce Type: replace-cross Abstract: Navigation and manipulation are core capabilities in Embodied AI, but training agents to perform them directly in the real world is costly, time-consuming, and unsafe.
arXiv:2609.38982v1 Announce Type: cross Abstract: Coding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital w...
SimSkill is a self‑evolving large‑language‑model agent designed for the SUMO traffic simulator. It continuously detects capability gaps, creates and solves environment‑grounded tasks, verifies solutions via an action–critic loop, and stores experiences in episodic, procedural, and semantic memory. Evaluations on two held‑out benchmarks across three LLM backbones show up to a 25‑percentage‑point improvement in verified success, with procedural and semantic memory contributing complementarily.