The Bitter Lesson Comes to Robotics
General purpose multimodal language models (LMs) are increasingly capable of directly controlling robotic arms (e.g., by commanding end effector poses) to manipulate their environment.
ProofPaper ↗
Key points
- To this end, we investigate the ability of frontier multimodal LMs to directly control physical robotic arms.
- We evaluate seven models at multiple reasoning-effort settings on six manipulation tasks taken from the RoboDojo benchmark.
- We find that current frontier models can directly control real robotic arms to perform a variety of manipulation tasks but are not yet precise or efficient enough to complete them within a practical action budget.
- These trends raise the possibility that the Bitter Lesson is coming to robotics; the capabilities of embodied general purpose LMs may one day be comparable to those of more specialized models.
Sources (1)
- [1]The Bitter Lesson Comes to RoboticsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 02:07 PM
General purpose multimodal language models (LMs) are increasingly capable of directly controlling robotic arms (e.g., by commanding end effector poses) to manipulate their environment.
To this end, we investigate the ability of frontier multimodal LMs to directly control physical robotic arms.
Extractive summary: sentences quoted from the sources.
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