Progress-Based Reward Models for Robotic Manipulation RL
ChongZzZhang · x · 2026-07-05
A reshared research discussion introduces "progress-based reward models" to tackle the challenge of reward design in reinforcement learning for robotic manipulation tasks, aiming to overcome the shortcomings of existing reward modeling methods. This topic falls under the scope of RL research for robotic manipulation training.
More from Embodied
- Embodied AI startups need a different operational discipline, investor says — ___Mufasaa · 2026-10-27
- π0.5 study: raising demos from 10 to 438 lifts Place Ball in Bowl success from 7% to 83% — stingning · 2026-10-27
- GPT-6 Astra controls dexterous hands zero-shot at just 5.2% success in 500 trials — stingning · 2026-10-27
- HandsOn benchmark: 66 verified tasks and 18,000 human demos for dexterous manipulation — stingning · 2026-10-27
- HandsOn dexterous-manipulation benchmark: best policy 29.1%, GPT-6 Astra just 5.2% — stingning · 2026-10-27
- DexPort: a handheld interface with a real robot hand for dexterous-hand data collection — pabbeel · 2026-10-27