sages_scitech2026_jgcd-1-compressed-1.pdf
slab.stanford.edu · 7,240 words · saved by 1 readers
N/A
Semantic Trajectory Generation for Spacecraft Rendezvous Using Large Language Models Yuji Takubo ∗ , Arpit Dwivedi † , Sukeerth Ramkumar ‡ , Luis A. Pabon § , Daniele Gammelli ¶ , Marco Pavone ‖ , Simone D’Amico ∗∗ Stanford University, Stanford, CA, 94305 Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and control is paving the way for onboard autonomous trajectory…
saved by
related reading
- acc_2026_impulsive_control_8.pdfslab.stanford.edu
- Next Generation Spacecraft Pose Estimation Dataset (SPEED+)purl.stanford.edu
- SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environmentsarxiv.org
- [2304.03442] Generative Agents: Interactive Simulacra of Human Behaviorarxiv.org
- LLM Powered Autonomous Agents | Lil'Loglilianweng.github.io
- [2304.03442] Generative Agents: Interactive Simulacra of Human Behaviorarxiv.org
- Building Effective AI Agents \ Anthropicanthropic.com
- Ch. 10 - Trajectory Optimizationunderactuated.csail.mit.edu
- How Claude Performs on Robotics Tasks \ Anthropicanthropic.com
- REx Lab: Homeroboticexplorationlab.org
- AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkersarxiv.org
- Stream of Search (SoS): Learning to Search in Languagearxiv.org