RACaP: Agentic Reasoning, Acting, and Coding as Policies for Evolvable Robot Learning

arXiv:2609.29394 · cs.RO · Submitted 2026-09-24 · Read on arXiv

cs.RO

Submitted: 2026-09-24

Updated: 2026-09-24

Project page: https://research.nvidia.com/labs/gear/aspire

Terminology

• Benchmark — 10× in this paper · explained in 2 episodes
• Trace — 9× in this paper · explained in 2 episodes
• Adaptation — 8× in this paper · explained in 1 episode
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Core — 5× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 5× in this paper · explained in 4 episodes
• Agent Harness — 3× in this paper · explained in 2 episodes
• Agentic Reasoning — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 3 episodes
• Generalizability — 3× in this paper · explained in 6 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Task Decomposition — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Agentic Framework — 2× in this paper · explained in 6 episodes
• Cross-Embodiment Transfer — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Affordance — 1× in this paper · explained in 1 episode
• Agentic Coding — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Modular Components — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
• GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
• CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
• VIA: Visual Interface Agent for Robot Control
• Inner Monologue: Embodied Reasoning through Planning with Language Models
• VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
• ABot-Claw: A Foundation for Persistent, Cooperative, and Self-Evolving Robotic Agents
• VIMA: General Robot Manipulation with Multimodal Prompts
• OpenVLA: An Open-Source Vision-Language-Action Model
• RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks
• Code as Policies: Language Model Programs for Embodied Control
• LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
• ASPIRE: Agentic /Skills Discovery for Robotics
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
• ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
• ReAct: Synergizing Reasoning and Acting in Language Models
• RRHF: Rank Responses to Align Language Models with Human Feedback without tears
• Playful Agentic Robot Learning
• Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents

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