PhysEvo: Astra Can Act, Let It
cs.RO, cs.AI
Submitted: 2026-10-06
Updated: 2026-10-06
Code: https://github.com/anonymous-report-421/GPT-as-Policy
Terminology
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- Show-Harness: Just a VLM Agent Can Play Robots
- RHO: Your Coding Agent is Secretly a Roboticist
- CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
- Transferring the Intelligence of VLMs to Robotic Control
- Automated Design of Agentic Systems
- RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning
- Inner Monologue: Embodied Reasoning through Planning with Language Models
- EmbodiSkill: Skill-Aware Reflection for Self-Evolving Embodied Agents
- OpenVLA: An Open-Source Vision-Language-Action Model
- Recursive Harness Self-Improvement
- Meta-Harness: End-to-End Optimization of Model Harnesses
- Code as Policies: Language Model Programs for Embodied Control
- Guava: Distilling Frontier VLMs into a Compact Agent through a Robotic Manipulation Harness
- PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution
- REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction
- AutoHarness: improving LLM agents by automatically synthesizing a code harness
- ASPIRE: Agentic /Skills Discovery for Robotics
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving