Ai Manipulation Agents

ManiAgent hits 86.8% on SimplerEnv vs pi0 55.7% and CogACT 51.3% — with zero robot fine-tuning. A deep-dive into the 3-agent architecture and why decomposition beats end-to-end.

Deep dive into ManiAgent's Perception Agent: Florence-v2 for zero-shot open-vocabulary object detection and AnyGrasp for 6-DoF grasp pose generation from point clouds. Build a complete Python perception module.

Comparing ALRM's two execution modes: CaP generates Python to call robot APIs in one pass, TaP uses ReAct to loop per tool call. Benchmark across 56 tasks and 10 LLMs to help you pick the right mode.

Run ds.py (DeepSeek-V3 subgoal decomposition) and main.py (OpenVLA on LIBERO). Implement a Temporal Verifier sliding window — SAP protocol achieving 79.6% LIBERO avg.

From 79.6% on LIBERO benchmark to physical robot deployment: calibrate camera-to-robot transforms, handle domain gap with real-world augmentation, and deploy the SAP pipeline on Franka/UR5 with latency under 100ms.