Guava: An Effective and Universal Harness for Embodied Manipulation
- Published
- Source
- arXiv
- Paper number
- 452
- Field
- Robotics
- arXiv ID
- 2606.18363
Key points
- ReAct-style closed-loop execution is decisively better than one-shot planning on long-horizon tasks.
- A semantic action space, such as grasp, align, and move, reduces the reasoning burden on the VLM compared with low-level joint control.
- Training on fewer than 2,000 simulation trajectories and then transferring zero-shot to the real world succeeds on three backbones, with 86 percent on in-distribution tasks and 92 percent on out-of-distribution tasks.
- RL post-training raises shell game success from 6.7 percent to 60 percent and place-all-red-objects success from 0 percent to 93.3 percent.
- The system exhibits error awareness and recovery behavior even in joint-limit or unreachable situations that were absent from training.
- It achieves a 75.6 percent overall success rate, outperforming GPT-5.4 at 70.2 percent and CaP-Agent0 at 62.7 percent.
Paper links
External research summaries. These are not HDATF publications or measured product results.